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32 results for “2000-2020”
Harmonising the land-use flux estimates of global models and national inventories for 2000-2020: background data
<p>This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files:</p> <p>1) "<strong>Global model</strong><strong>s</strong> <strong>land CO2 </strong><strong>data 2000-2020</strong>" (MS Excel Format), including for each country data for:</p> <p>a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). </p> <p>b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest.</p> <p>The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022).</p> <p>2) “<strong>National inventories LULUCF data 2000-2020</strong>” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022).</p> <p>3) “<strong>Processing steps for DGVM results”,</strong> describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). </p> <p>4) “<strong>Intact and non-intact forest maps</strong>”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution.</p> <p>5) "<strong>IntactAndNonIntactForest_0.5deg_script.js</strong>", the Google Earth Engine Java script to produce the forest maps (.js/text format)</p> <p>For further details, please refer to:</p> <p>Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data.</p> <p>Other references:</p> <p>Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900.</p> <p>Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.</p> <p> </p> <p> </p>
MOSEV: A global burn severity database from MODIS (2000-2020)
<p>To advance in the fire discipline as well as in the study of CO<sub>2</sub> emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products scenes since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning.</p>
All-weather 1km land surface temperature at global scale from 2000-2020 from MODIS data
<p>All-weatherLand Surface Temperature product (2000-2020): LSTs from Moderate Resolution Imaging Spectroradiometer(MODIS)/Terra have been produced. The LST data were generated by integrating multiple data from MODIS, reanalysis, and ground in situ measurements using meachine learning method. </p> <ul> <li>The dataset is organized by year.</li> <li>The data is stored in tif format.</li> </ul>
MGP: a new 1-hourly 0.25° global precipitation product (2000-2020) based on multi-source precipitation data fusion
<p>The multi-source merged global precipitation product (MGP; 0.25°/ hourly; 2000-2020; 60°N/S), which takes advantage of the complementary strengths of satellite, reanalysis, and gauge data to obtain reliable precipitation estimates over the global land surface, provides a new higher-quality precipitation dataset for data users to realize their respective research purposes and the social and economic activities. The data developers hope that MGP will play an important role in a variety of science communities (e.g., hydrology, meteorology, climatology, ecology, and agriculture).</p>
Data for Greenhouse gas budgets of Central and West Asia (2000-2020)
<p>The data and code provided are utilized to support the conclusions of "Greenhouse gas budgets of Central and West Asia (2000-2020): A significant net source to the atmosphere" (submitted). The dataset comprises the processed ensemble mean values at both national and regional scales. Please note that in the dataset, "NBP" represents the NBP fluxes from TRENDY v11 models, "Wetland CH4" represents the wetland CH4 emission estimates from Terrestrial Biosphere Models in the Global Methane Budget, "Natural Soil N2O" represents the natural soil N2O emissions as estimated by NMIP2 models, and "Inversions" represents the inversion fluxes for each GHG. More detailed information about the meaning and data sources of each sector can be found in the Methods section of the manuscript and in Supplementary Information S1.</p>
Data for Greenhouse gas budgets of Central and West Asia (2000-2020)
<p>The data and code provided are utilized to support the conclusions of "Greenhouse gas budgets of Central and West Asia (2000-2020): A significant net source to the atmosphere." The dataset comprises the processed ensemble mean values at both national and regional scales. Please note that in the dataset, "TD_total" represents the inversion fluxes for each GHG, "NBP" represents the NBP fluxes from TRENDY, "Wetland_ch4" represents the wetland CH4 emission estimates from Terrestrial Biosphere Models in the Global Methane Budget, and "Soil_n2o" represents the soil N2O emissions as estimated by NMIP2 models. More detailed information about the meaning and data sources of each sector and subsector can be found in the Methods section of the manuscript and in Supplementary Information S1.</p>
Recent descriptions of aquatic insects in Europe (2000-2020)
<p>The following database contains information on recently described species of aquatic insects described between 2000 and 2020 in the European continent (including western Russia, Cyprus and Turkey).</p>
A dataset for the threat degree map of human expansion to biodiversity in Asia over 2000-2020
<p>A dataset for the threat degree map of human expansion to biodiversity in Asia over 2000-2020</p> <p>The dataset includes three inputs (density maps of patch number, patch area, and number of threatened species involved in the patches of human expansions), and one output (i.e. final result, a threat degree map). The threat degree map was calculated as the geometric mean of three indicators(inputs) (see corresponding publication).</p> <p>The threat degree map (output file) is compressed GeoTiff files (*.tif). The spatial resolution is 1km×1km. The threat degree map represents the threat degree of human expansion (referring to cropland and artificial surface expansion patches over 2000-2020) to biodiversity. The value of threat degree map varied from 0% to 100%, with a larger value indicating a higher threat of human expansion to biodiversity.</p> <p>Cite as<br>Chao Yang, et al. (2023). Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020. Research, 6, 0226. DOI:10.34133/research.0226</p>
Quality of Evidence submitted to NICE Technology Appraisals (2000-2020)
<p>This file provides a data set on 409 Technology Appraisals (TAs) issued by NICE between March 2000 when TA1 was published and September 11, 2019 when TA 600 was published. For each of these TAs, there are analyses including a score for their Data Quality, QoL Data Quality, Quality of Submitted Evidence, Overall Quality of Evidence for decision, and Quality of Submitted RCTs. A legend is in the file's second tab.</p>
ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020
This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.
SiB4 Modeled 0.5-degree Carbonyl Sulfide Vegetation and Soil Fluxes, 2000-2020
This dataset provides outputs from the Simple Biosphere Model (v 4.2). Products include hourly 0.5-degree gridded fluxes of gross primary productivity (GPP), respiration, carbonyl sulfide (COS) uptake by vegetation and soil, along with conductance of COS (apparent mesophyll and total), stomatal conductance of water and partial pressure of CO2 in the canopy air space, leaf surface, interior and chloroplast. The data are separated by plant functional type (PFT). Fluxes have dimensions of latitude, longitude, time, and plant functional type. Model output spans 53N to 90N latitude and 180W to 180E longitude over years 2000 to 2020. The data are provided in NetCDF version 4 format.
Daily meteorological data extracted from ERA5 based on DHS-GIS (2000-2020)
<p>Daily meteorological dataset extracted from ERA5 based on DHS-GIS (2000-2020). It has three meteorological variables: relative humidity (1000hpa), daily 2m temperature, daily maximumu 2m temperature, daily minimum 2m temperature, and total precipitation. </p> <p> </p>
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