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opencc-by-4.0Dec 2023View details →

A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19

Overview This dataset is the repository for the following paper submitted to Data in Brief: Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023). The Data in Brief article contains the supplement information and is the related data paper to: Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023). Description/abstract The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.  Folder structure The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:  “code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data. “MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023. “mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”). “yield_productivity” contains .csv files of yield information for all countries listed above. “population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods). “GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data. “built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.  Code structure 1_MODIS_NDVI_hdf_file_extraction.R  This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent. 2_MERGE_MODIS_tiles.R In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.).  Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged"). 3_CROP_MODIS_merged_tiles.R Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.  The repository provides the already clipped and merged NDVI datasets. 4_TREND_analysis_NDVI.R Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November).  December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3. To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.  5_BUILT_UP_change_raster.R Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.  6_POPULATION_numbers_plot.R For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.  7_YIELD_plot.R In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.  8_GLDAS_read_extract_trend The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).  Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area. From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year). From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.  (9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis. ___ Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf ___ Acknowledgements I would like to thank three anonymous reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia at the University of Basel under grant number TMPFP2_217358.   __ All data underlying the results of this article are publicly available on the internet: GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023);  Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023); FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023); Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023); Population development:  FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023);  the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023);  Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023); Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023); MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023). Competing interests statement: The author declares no conflict of interest. The author has no relevant financial or non-financial interests to disclose. Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text. Ethical approval: This article does not contain any studies with human participants performed by any of the authors. Informed consent: This article does not contain any studies with human participants performed by any of the authors.

opencc-by-4.0Dec 2021View details →

Code for "New land-use-change emissions indicate a declining CO2 airborne fraction"

Data and programming scripts for reproducing the results from the Nature publication titled: "New land-use-change emissions indicate a declining CO2 airborne fraction". Authors: Margreet J. E. van Marle*, Dave van Wees*, Richard A. Houghton, Robert D. Field, Jan Verbesselt, and Guido R. van der Werf * These authors contributed equally. DOI: https://doi.org/10.1038/s41586-021-04376-4   This dataset includes the following (All files are preceded by "Marle_et_al_Nature_AirborneFraction_"): - "Datasheet.xlsx": Excel dataset containing all annual and monthly emissions and CO2 time series used for the analysis, and the resulting airborne fraction time series. - "Script.py": BEFORE RUNNING THE SCRIPT: change the 'wdir' variable to the directory containing the provided script and files. NOTE: This script requires the Python module: 'pymannkendall' Python script used for reproducing the results and figures from the paper. The provided Datasheet.xlsx file and the .zip and .npz files are required for this program. In case all these files are found by the script, it should run within several seconds. Successful execution of the script will save Figures 1-4 from the main text and print the data from Table 1. In case script execution takes longer, please check if the .xlsx, .zip and .npz files are correctly present in the assigned 'wdir' directory. Otherwise the script will start recalculating these files, which might take a while (see notes below). - "MC10000_MK_ts_TRENDabs.zip": .zip file containing all results from the Monte-Carlo simulation for trend estimation for Figure 3 (calculated using Python function 'calc_AF_MonteCarlo()'). This .zip file contains multiple .npz files for different emission scenarios and data treatments. This .zip file is managed by the Python script function 'calc_AF_MonteCarlo_filemanager()', there is no need to unzip the file manually. In case the .zip file is not found by the Python script (e.g. because the .zip file was unpacked manually and deleted), the program will start recalculating and save a new .zip file. This can take several minutes dependent on the computer used. Recalculated results could differ very slightly due to the random factor in the Monte-Carlo approach, even though the 10,000 iterations bring this variation to a minimum. - "MC1000_MK_run50x50_TRENDabs.npz": .npz file containing the Monte-Carlo results used for producing Figure 4 (calculated using Python function 'calc_AF_MonteCarlo_ARR()'). In case the .npz file is not found by the Python script (e.g. because it was deleted or not downloaded), the program will start recalculating and save a new file. This can take around 30 hours(!) dependent on the computer used. Recalculated results could differ slightly due to the random factor in the Monte-Carlo approach. - "tol_colors.py": Additional Python module used in script.py, required for producing the colors used in the Main text figures. Source: https://personal.sron.nl/~pault/ - Figure files: Figures 1-4 from the Main text saved as .pdf files. Figure 3 is saved as three independent panels. The Figures are also reproduced by script.py if executed successfully.

opencc-by-4.0Jul 2024View details →

Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change

Environmental and biodiversity data associated with the article: Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. Oikos. https://doi.org/10.1111/oik.10697 Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables. Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file. <ul> <li>Danish Common Bird Monitoring programme – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</li> <li>E-OBS European gridded climate data – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research Atmospheres, 113(20). https://doi.org/10.1029/2008JD010201</li> <li>AVONET bird traits data – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul>

opencc-by-4.0Oct 2024View details →

Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.

We reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here.

opencc-by-4.0Jan 2022View details →

Supplementary material for "Long-term trends of reproductive success of black grouse Lyrurus tetrix in the southern Swiss Alps in relation to changes in climate and land use"

Abstract Breeding success of an Alpine black grouse Lyrurus tetrix population in southern Switzerland was monitored from 1981 to 2020. This long-term dataset allows exploring relationships of reproductive rates with climate and habitat, which have shown marked changes during this period. Over the 40 years, the average elevation of black grouse breeding sites increased by around 100 m in Central/Southern Ticino but showed only a slight increase in Northern Ticino, where black grouse occur at higher elevations. Average reproductive rates in Northern Ticino remained constant throughout the study period but declined in Central/Southern Ticino. Relationships between reproductive success and weather as well as habitat variables were analysed with a multiple regression model. Temperature during the early chick-rearing phase and the time of egg-laying was positively correlated with reproductive rate. Correlations between reproductive rates and precipitation were less clear, and only small proportions of the variance in reproductive rates could be explained by precipitation. Brush forest explained the greatest amount of variation in reproductive rate (6.2%). Forest, alpine agricultural areas, and unproductive vegetation all showed a positive relationship with reproductive rate, but the proportion of the variance explained was small. Year (5.1%) and its interaction with region (2.3%) explained considerable amounts of the variance. While in Northern Ticino reproductive success did not show a negative trend when correcting for weather and habitat changes, there remained a negative trend over the years in Central/Southern Ticino. Despite the positive correlations of reproductive rate with temperature, increasing temperatures do not appear to have improved reproductive success, likely as a result of habitat changes that forced black grouse towards higher elevations. Changes in reproductive success were limited to the southern region, indicating deteriorating conditions at the edge of the distribution range.

opencc-by-4.0Feb 2022View details →

Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes

Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.

opencc-by-4.0Mar 2018View details →

Territorial land use data relative to Pesa Basin ( Tuscany) and its recent changes ( 2016- 2007)

Data and elaborations are provided by IBIMET CNR , Accademia Georgofili and <a href="http://www.cbmv.it/">Consorzio di Bonifica 3 Medio Valdarno. </a>

opencc-by-4.0May 2019View details →

GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020

Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146

opencc-by-4.0Apr 2023View details →

Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"

This data archive contains model runs and data analysis files to the research article Bioenergy-induced land-use change emissions with sectorally fragmented policies by Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler published in Nature Climate Change (2023). ModelRuns_remind (directory) contains all REMIND model runs of the scenarios underlying the paper. ModelRuns_magpie (directory) contains all MAgPIE model runs of the scenarios underlying the paper. DataAnalysis (directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper. ScenarioMapping.pdf  contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).

opencc-by-4.0May 2023View details →

Direct and indirect effects of climate and land use change on food webs in lakes and streams

Here, we provide the data and code necessary to reproduce the workflow and analysis in: Barbosa and Siqueira. Direct and indirect effects of climate and land use change on food webs in lakes and streams. A preprint is available at https://doi.org/10.1101/2022.04.18.488700 We compiled multicontinental data to investigate how climate and land use change are related to the structure of freshwater food webs, considering the inherent differences in lentic and lotic ecosystems. We analyzed the direct and indirect relationships between land use intensity, and temperature and precipitation changes, and food webs using multi-group structural equation modeling. Freshwater food webs were obtained from three sources: the Mangal interaction database, using the rmangal package in R, the GlobAl databasE of traits and food Web Architecture (GATEWAY) version 1.0, and the Interaction Web Data Base (IWDB). We also included food webs acquired from a search in the Web of Science Core Collection. Land use data was compiled from the global ESA CCI database, an annually generated land cover product at 300 m resolution for the period 1992 – 2015. Climate data was compiled from the TerraClimate database, a monthly generated product for climate and climatic water balance for global terrestrial surfaces at ~ 4 km for the period 1958 – 2015.

opencc-by-4.0May 2023View details →

High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes

A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356. The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv et al. (2023). Data description Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes. To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover. Land surface category (LSC) These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories. Discrete LSC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 11 </td> <td> Tree (leaf-on) </td> </tr> <tr> <td> 12 </td> <td> Shrubland (leaf-on) </td> </tr> <tr> <td> 13 </td> <td> Grassland </td> </tr> <tr> <td> 14 </td> <td> Woody vegetation (leaf-off) </td> </tr> <tr> <td> 15 </td> <td> Wilted herbaceous vegetation </td> </tr> <tr> <td> 21 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 22 </td> <td> Water </td> </tr> <tr> <td> 24 </td> <td> Built-up </td> </tr> </tbody> </table> In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype. Land use land cover After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system. The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions. Discrete LC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 10 </td> <td> Tree cover </td> </tr> <tr> <td> 20 </td> <td> Shrubland </td> </tr> <tr> <td> 30 </td> <td> Grassland </td> </tr> <tr> <td> 40 </td> <td> Cropland </td> </tr> <tr> <td> 50 </td> <td> Built-up </td> </tr> <tr> <td> 60 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 80 </td> <td> Permanent water bodies </td> </tr> <tr> <td> 90 </td> <td> Herbaceous wetland </td> </tr> </tbody> </table> Land use land cover change Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change. Files The zip files contain the following data: <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “tile-year-month.tif”. References Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963                                                                   Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. Zanaga, D., et al.., 2021. ESA WorldCover 10 m 2020 v100. <a href="https://doi.org/10.5281/zenodo.5571936 ">https://doi.org/10.5281/zenodo.5571936 </a>

opencc-by-4.0Jul 2023View details →

Data and code: Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology

Zip folder conaining the data and code that support the findings of Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology. The Data_code.zip folder contains four subfolders with the following files: <ul> <li>Data_raw <ul> <li>AgriDiv_data.csv</li> <li>AgriDiv_metadata.docx</li> <li>Species_data.csv</li> <li>Species_metadata.docx</li> <li>Landscape_data.csv</li> <li>Landscape_metadata.docx</li> </ul> </li> <li>Data_derived <ul> <li>RIA_RSR_effect_sizes.csv</li> <li>MSA_effect_sizes.csv</li> </ul> </li> <li>Data_output <ul> <li>Response_estimation.csv</li> </ul> </li> <li>R_scripts <ul> <li>01_Effect_size_calculation.R</li> <li>02_Null_model_analysis.R</li> <li>03_Model_selection.R</li> <li>04_Model_analysis.R</li> <li>05_Response_estimation.R</li> <li>06_Figures.R</li> <li>README.md</li> </ul> </li> </ul>

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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.

openMay 2015View details →

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Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales

At high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between e.g. the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms are poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long-term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change causing coastal water darkening has gradually delayed the spring bloom up to 1990 after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land-ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.

opencc-zeroJan 2024View details →

dryad40/100

Land use change converts temperate dryland landscape into a net methane source

Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12% respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally.

opencc-zeroMar 2024View details →

opencc-by-4.0Apr 2024View details →

A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)

This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production. The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario. For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a> This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.   This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).   ------------------------- Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a> License: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/) Copyright to display of the datasets:  “Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).” 2 Main files : <ul> <li>SSP1LEonLCM19_LUC_2019.tif : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland). Contributions to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li>SSP1LEonLCM19_LUC_2050.tif : land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a>. This version of the datasets only includes 100m cells with land use change (14% of Scotland). Contributions to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul>   Main references: Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a> Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4.  Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government. Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government. Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk) FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman) Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27). Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010).  Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889.  <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a>   <a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a>

opencc-by-4.0Jan 2022View details →

Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces

Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.

opencc-by-4.0Mar 2022View details →

Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)

This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX). The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute  for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France);  Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015. Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant. The objectives of CLIMAX  include  better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate. <ol> <li> EXPERIMENT DESIGN </li> </ol> We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005). We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022). We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named historical CO2 (hist CO2); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named constant CO2 (const CO2). We ran both CO2 experiments under Land Use Change (LUC) and Potential Natural Vegetation (PNV) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. LUC historical CO2; 2. LUC constant CO2; 3. PNV historical CO2; 4. PNV constant CO2  (Rezende et al., 2022). 2. DATA DESCRIPTION The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs. 2.1 SOFTWARE The data were manipulated, worked, standardized, converted using the software: Climate Data Operators (cdo) version 1.7.0, Grid Analysis and Display System (Grads) (<a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>) version 2.0.2, and RStudio Desktop version 1.3.1093 (R Core Team, 2020), through command lines and several scripts developed for this purpose. The figures were generated  with Grads, and RStudio, and some images were enhanced  with Gimp version 2.8.22. All the software used is freeware.    2.2 PRIMARY DATA FROM SIMULATIONS Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning  over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010. Variables: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment). The naming of the files is according to the following rules: DGVM_forcing_vegetation cover_CO2 concentration_attribute DGVMs;     InLand (INLAND)     LPJ-G (LPJ-GUESS)     LPJmL (LPJmL4)     ORCHI (ORCHIDEE) forcings:      gld - GLDAS     gsw – GSWP3     wat – WATCH+WFDEI   vegetation cover:     LU – Land Use Change     PNV – Potential Natural Vegetation   CO2 concentration:     CO2 – historical CO2     noCO2 – constant CO2 = 278 ppm   variables:     E – evaporation     Et – transpiration     gpp – Gross Primary Productivity     npp - Net Primary Productivity (not used in the experiment)   Example:     inLand_gld_LU_noCO2_E.nc    2.3 SUPPLEMENTARY DATA These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude). Files are named  according to the following rules: variable_season_forcing_DGVM_vegetation cover CO2 concentration_region variable:     inter – loss by interception   season:     D – dry season     R – rainy season   forcings:                 gl - GLDAS                gs – GSWP3                wa – WATCH+WFDEI   DGVMs:     in - INLAND      lg - LPJ-GUESS             lm - LPJmL4             or – ORCHIDEE   vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example:     inter_D_gl_in_LC_SA.nc 2.4 PROCESSED DATA Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).   Files are named according to the following rules:   season_DGVM_forcing_vegetation cover CO2 concentration_region season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:             Gl - GLDAS            Gs – GSWP3            Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   Example: D_in_Gs_LN.nc 2.5 DERIVED DATA   The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1).    <table> <tbody> <tr> <td>                                   WUE = GPP / Tr </td> <td> (Eq. 1) </td> </tr> </tbody> </table>   These data refer to the study region: southern Amazon and apply to only one scenario:  Land Use Change and historic CO2. Files are named  naming of according to the following rules:   Variable:     WUE – Water Use Efficiency   season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:                 Gl - GLDAS                Gs – GSWP3                Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example: wue_D_lm_Gl_LC_SA.nc   How to cite this work: Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. References <a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015. Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a> Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.               Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg. Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6. Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>. Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637 Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36