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1,103 results for “moisture”
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data
<p>This repository serves as hub for the single <strong><em>GPM_API data set repositories</em></strong> related to the publication</p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <ul> <li>GPM_API 2015: <a href="https://doi.org/10.5281/zenodo.6353260">10.5281/zenodo.6353260</a></li> <li>GPM_API 2016: <a href="https://doi.org/10.5281/zenodo.6413889">10.5281/zenodo.6413889</a></li> <li>GPM_API 2017: <a href="https://doi.org/10.5281/zenodo.6413905">10.5281/zenodo.6413905</a></li> <li>GPM_API 2018: <a href="https://doi.org/10.5281/zenodo.6413907">10.5281/zenodo.6413907</a></li> <li>GPM_API 2019: <a href="https://doi.org/10.5281/zenodo.6413909">10.5281/zenodo.6413909</a></li> <li>GPM_API 2020: <a href="https://doi.org/10.5281/zenodo.6413911">10.5281/zenodo.6413911</a></li> </ul> <p> </p> <p>The related article can be found here:<br> Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
Project 2021/05/X/ST8/00114: Introduction of moisture content as a parameter of the breakage probability function of granular biomass - Dataset
<p>This dataset contains the results of research conducted at Purdue University, Agricultural&Biological Engineering during research stay in the period 15.11.2021 - 14.02.2022. The aim of the research was to investigate and analyze the breakage probability of different types of grains for various moisture content levels: 10%, 14%, 18%, 22% and 26 % and particle sizes. The experimental part involves compression tests of selected grains (rice, corn) and specific breakage energy determination. In the result the breakage probability equations in dependance of moisture content and particle size were developed.</p> <p>This dataset contains:</p> <ol> <li>the results of initial moisture content measurement,</li> <li>the results of moisture content measurement after wetting,</li> <li>The results of bulk density measurement,</li> <li>the results of compression tests for low compression rate (1.25 mm/min)</li> <li>the results of compression tests for high compression rate (125 mm/min)</li> </ol>
Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects
<p>Controlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta-analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer-reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy-logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium-textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse- or fine-textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse- and medium-textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy-logic-based approach revealed that while potentially suitable areas are mostly concentrated in the well studied U. S. Midwest, these areas also exist in other regions where CDSI may warrant further study.</p>
Radiocarbon and soil properties along the Kalahari moisture gradient in Botswana
<p>This dataset presents radiocarbon data from four sites located in Botswana. The dataset first presents general information about the sites (on the tab "site"), followed by more detailed information (on the tab "profile") about all sampling locations. On the 'layer' tab, information about selected soil properties and the radiocarbon values are shown.</p> <p>The dataset is part of the International Soil Radiocarbon Database (ISRaD) and associated with the following publication: Dintwe et al. (2015) Soil organic C and total N pools in the Kalahari: potential impacts of climate change on C sequestration in savannas, Plant Soil, 396_27-44, doi: 0.1007/s11104-014-2292-5.</p> <p> </p>
Changes in trait covariance along an orographic moisture gradient reveal the relative importance of light- and moisture-driven trade-offs in subtropical rainforest communities
<p>•<span> </span>A range of functional trait-based approaches have been developed to investigate community assembly processes, but most ignore how traits covary within communities. </p> <p>•<span> </span>We combined existing approaches (community-weighted means [CWMs] and functional dispersion [FDis]) with a metric of trait covariance to examine assembly processes in five angiosperm assemblages along a moisture gradient in Australia's subtropics. In addition to testing hypotheses about habitat filtering along the gradient, we hypothesised that trait covariance would be strongest at both ends of the moisture gradient and weakest in the middle, reflecting trade-offs associated with light capture in productive sites and moisture stress in dry sites.</p> <p>•<span> </span>CWMs revealed evidence of climatic filtering, but FDis patterns were less clear. As hypothesised, trait covariance was weakest in the middle of the gradient, but unexpectedly peaked at the second driest site due to the emergence of a clear drought tolerance – drought avoidance spectrum. At the driest site, the same spectrum was truncated at the 'avoider' end, revealing important information about habitat filtering in this system.</p> <p>•<span> </span>Our focus on trait covariance revealed the nature and strength of trade-offs imposed by light and moisture availability, and complemented insights gained about community assembly from existing trait-based approaches. </p>
Combined_ST_SM_Changes_Impacts Soil moisture observations
<p>There are 956 observational soil moisture data files with detailed information about the files in ReadMe.txt.</p>
WRF dataset for soil moisture initialization experiments
<p>Modeling results of "Role of Land–Atmosphere Interaction in the 2016 Northeast Asia Heat Wave: Impact of Soil Moisture Initialization", by Yoon et al. (submitted). Data contains the modeling outputs (500GPH, SAT and soil moisture) from CTL and LIS experiments. Detailed description can be founded in the research paper.</p>
IODP Expedition 352 Moisture and Density
<p>Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.</p>
IODP Expedition 351 Moisture and Density
<p>Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.</p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
A global 1-km surface soil moisture product from 2000 to 2020
<p>Soil moisture is one of the essential climate variables, and it controls the water, carbon, and energy exchanges between land and the atmosphere. Accurate and detailed knowledge of the spatial and temporal distribution of soil moisture is critical for various earth system applications. A long-term global 1-km daily surface soil moisture product has been generated from 2000 to 2020, as part of the Global Land Surface Satellite (GLASS) products suite. This product (GLASS SM) was generated mainly from the GLASS albedo, LST, and LAI products, ERA5-Land reanalysis soil moisture product, and auxiliary datasets based on an ensemble machine learning model. Site-independent validation results showed that the median unbiased RMSE and R for the model was 0.052 m<sup>3</sup>/m<sup>3</sup> and 0.74, respectively.</p> <p>Data values contained in the GLASS SM product represent the volumetric water content of the uppermost soil layer (0–5 cm). The files are stored in the Sinusoidal projection and provided in Geo Tiff format. “Nodata” value is set to -9999.</p>
Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean since 1996
<p>Here is a dataset of live fuel moisture content (LFMC, computed as the water mass over dry mass of living shoots) time series, collected in the French Mediterranean area by the French National Forest Organization (“Office National des Forêts”) for operational fire prevention. A network of 53 sites (called the "Reseau Hydrique" network) were sampled, among which 35 are geolocalized for a maximum period extending from 1996 to 2016. For each site and year available, LFMC is measured during the summer season on shrub species (between one and three species per site) at a weekly to biweekly frequency depending on site and year.</p> <p>The dataset can be used to validate or calibrate fire danger model, assess remote sensing drought indices and understand the physiological and climatic determinants of LFMC. There are 584 site*year data (a total of 22787 individual data) for several shrub species of the French Mediterranean area.</p> <p>From the raw dataset, researchers from the Ecology of Mediterranean Forest Unit at INRA (French National Institute for Research in Agronomy) of Avignon (France) have produced an improved dataset that includes corrections, outlier identifications and error estimations. Preliminary validation assessments of the data quality were also produced.</p> <p>A data paper describing in detail the method and all the modifications, error estimations and evaluations of the raw dataset that were performed is under review in Annals of Forest Science. Both the raw and improved datasets are made available on Zenodo (Cabane et al 2017, DOI 10.5281/zenodo.162978).</p> <p>The attached dataset consists of four tables:</p> <ol> <li>The first table (<em>LFMC_final_Table.csv</em>) contains the live fuel moisture content (LFMC) on a dry weight basis (see Supplementary S1 for details). These are the robust estimates of LFMC and their associated standard errors which were both estimated from raw data with the method fully described above described in a data paper under revision (Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Each row in the table describe the LFMC at a given date, for a given species and a given site. The table has eleven columns. The first eight columns indicate the site identifier (SiteCode and SiteName), the species (Species), a unique identifier for a given species at a given site (SitexSpecies), the date (Date, Year, Month, Day of Year). The last three columns are respectively the robust LFMC (labelled RobustLFMC), the standard error <em>SE</em> (labelled RobustStandErrLFMC) and the number of valid measurements that were not identified as outliers (labelled RobustNval). RobustStandErrLFMC, and that can be used to estimate confidence limits depending on the desired confidence rate.</li> </ol> <p> </p> <ol> <li>The second table (<em>RainTable.csv</em>) contains rainfall measurements. The site identifiers are given (SiteCode and SiteName) and the rainfall amount (rainfall) corresponding to rainfall occurring between the day of year of the previous measurement (PreviousDoy) and the day of year when the measurement was performed (Doy). The last column enables to identify the doubtful measurements (RainFlag = 1), when the discharge of the gauge during the previous measurement was uncertain.</li> </ol> <p> </p> <p> </p> <ol> <li>The fourth table (<em>InfoSite_ReseauHydrique.csv</em>) contains a basic description of each site. It includes the identifier of the site (SiteCode and SiteName), the coordinates of the site in WGS84 (Longitude and Latitude), a flag indicating whether the site is still active (1= active, 0= inactive), the names of measured species (SpeciesName1 and up to SpeciesName3), the first and last year of measurement, as well as the number of measurement year available, for each species (StartYear, EndYear, NbYears).</li> </ol> <p> </p> <ol> <li>The third table contains raw data as produced by the French National Forest Organization (<em>LFMC_raw_Table.csv</em>). The first twelve columns indicate site name, species name, and date, as in the first table. The six following columns indicate individual LFMC values (LFMC1 to LFMC5), and the mean LFMC value (FFSLFMC) released by the French Forest Service. The last six columns correspond to flags identifying outliers (LFMC1Flag to LFMC5Flag). Flags were attributed either manually or automatically (see Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Missing values (e.g. following an unforcasted rain event, see Methods) were represented by the symbol “NA“ (Not Available).</li> </ol> <p>Note that the initiative was funded by a French organization dedicated the protection of the Mediterranean forest (The "Délégation à la Protection de la Forêt Méditerranéenne") and the raw dataset is available on a French website (http://www.reseau-hydrique.org/). However the raw dataset is not fully adapted to scientific purposes for several reasons. The dataset is not referenced (<em>i.e.</em> does not have a DOI) and its description is in French. In addition, the labels of sampling sites have evolved over time and some species were given a vernacular name. Finally, raw data are expressed on fresh mass basis (instead of dry mass as generally done in scientific studies) and present some outliers, duplications and inconsistencies. Additionally, uncertainties were not provided in the raw datasets. This is why INRA researchers recommend the usage of the improved dataset. In the forthcoming month, additional data regarding the environmental description, the ecology and history of the sites will be provided.</p> <p> </p> <p>Martin-StPaul, N; Pimont, F; Dupuy JL; Rigolot E; Ruffault J; Fargeon H; Cabane E; Duché Y; Savazzi R; Toutchkov M. Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean area since 1996. Under revision in Annals of Forest Science.</p> <p> </p>
Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States: Data
<p>The data in this repository are associated with the manuscript from Huber et al. (2024) titled "Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States" in the Journal of Geophysical Research: Atmospheres. Additional information regarding these data can be found in the attached readme file.</p>
Global soil moisture simulated by SoilClim and mHM models at 0.5° resolution for the 1980–2022 period
<p>This deposit contains two .zip archives (SoilClim_AWR_2m_1980_2022.zip and mHM_SM_2m_1980_2022.zip), each containing 1570 GeoTIFF files. </p> <p>The file SoilClim_AWR_2m_1980_2022.zip contains 10-day simulations of relative available water (AWR), where 100% represents the full field capacity and 0% represents the wilting point, produced the SoilClim water balance model for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p> <p>The file mHM_SM_2m_1980_2022.zip contains 10-day simulations of soil moisture (SM), produced the mesoscale Hydrologic Model (mHM) for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p>
Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </p>
Figure 1 in Effect of degree of water stress on growth and fecundity of velvetleaf (Abutilon theophrOsti) using soil moisture sensors
Figure 1. Soil moisture content in pots was measured using (A) Meter Group 5TM moisture sensors and (B) Em50 data loggers to determine degree of water stress on Abutilon threophrasti in a greenhouse study conducted at the University of Nebraska–Lincoln.
TreeGOER Global Zones: Global atlas for the Climatic Moisture Index (CMI), Maximum Climatological Water Deficit (MCWD) and the number of months with average temperature > 10 degrees C (Tmo10)
<p>The <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database documents the environmental ranges for 48,129 tree species and is available from files archived at <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparation of this database from 30 arc-second global grid layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. The atlas from this archive was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p><strong>TreeGOER</strong> includes a file (<em>TreeGOER_Tmo10_classes.txt</em>) that documents the distribution of species in zones defined by the number of months with average temperature > 10 degrees C. <strong>TreeGOER</strong> also includes a file (<em>TreeGOER_CMI_classes.txt</em>) that documents the distribution of species in zones defined by the Climatic Moisture Index (CMI). The atlas provided here shows the global distribution of the Tmo10 zones and CMI zones at high resolution on six sheets each, including three sheets in the northern hemisphere and three sheets in the southern hemisphere.</p> <p>The atlas also includes six sheets that show the global distribution of the Maximum Climatological Water Deficit (MCWD), another environmental variable covered by the <strong>TreeGOER</strong> database.</p> <p> </p> <table> <tbody> <tr> <td><strong>Zone</strong></td> <td><strong>Classes</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>Tmo10</td> <td> Tmo10 = 12 + Bio06 >= 18</td> <td>tropical (minimum temperature of coldest month 18 degrees C or higher)</td> </tr> <tr> <td> </td> <td>Tmo10 = 12 + Bio06< 18</td> <td>tropical (minimum temperature of coldest month less than 18 degrees C)</td> </tr> <tr> <td> </td> <td>8 ≤ Tmo10 < 12</td> <td>subtropical</td> </tr> <tr> <td> </td> <td>4 ≤ Tmo10 < 8</td> <td>temperate</td> </tr> <tr> <td> </td> <td>1 ≤ Tmo10 < 4</td> <td>boreal</td> </tr> <tr> <td> </td> <td>Tmo10 < 1</td> <td>polar</td> </tr> <tr> <td>CMI</td> <td>CMI ≥ 0.5</td> <td>P >= 2 * PET</td> </tr> <tr> <td> </td> <td>0 ≤ CMI < 0.5</td> <td>PET <= P < 2 * PET</td> </tr> <tr> <td> </td> <td>−0.35 ≤ CMI < 0</td> <td>0.65 <= P/PET < 1</td> </tr> <tr> <td> </td> <td>−0.5 ≤ CMI < −0.35</td> <td>dry sub-humid</td> </tr> <tr> <td> </td> <td>−0.8 ≤ CMI < −0.5</td> <td>semi-arid</td> </tr> <tr> <td> </td> <td>−0.95 ≤ CMI < −0.8</td> <td>arid</td> </tr> <tr> <td> </td> <td>CMI < −0.95</td> <td> hyper-arid</td> </tr> <tr> <td>MCWD</td> <td>MCWD ≤ -100</td> <td> </td> </tr> <tr> <td> </td> <td>−200 ≤ MCWD < −100</td> <td> </td> </tr> <tr> <td> </td> <td>−400 ≤ MCWD < −200</td> <td> </td> </tr> <tr> <td> </td> <td>−600 ≤ MCWD < −400 </td> <td> </td> </tr> <tr> <td> </td> <td>−800 ≤ MCWD < −600</td> <td> </td> </tr> <tr> <td> </td> <td>−1000 ≤ MCWD < −800</td> <td> </td> </tr> <tr> <td> </td> <td>−1250 ≤ MCWD < −1000</td> <td> </td> </tr> <tr> <td> </td> <td>−1500 ≤ MCWD < −1250</td> <td> </td> </tr> <tr> <td> </td> <td>−1750 ≤ MCWD < −1500 </td> <td> </td> </tr> <tr> <td> </td> <td>−2000 ≤ MCWD < −1750 </td> <td> </td> </tr> <tr> <td> </td> <td>−2500 ≤ MCWD < −2000 </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>A fourth map series in the atlas combines information from the Climatic Moisture Index with the distribution of 52,602 cities that were included in the CitiesGOER database, available from <a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a><a name="_Hlk141002106"></a><br></p> <p>Maps were created from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas), <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</p> <p>For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>When using the TreeGOER Global Zones atlas in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> </ul> <p> </p> <p>The development of the TreeGOER Global Zones atlas (including development of version 2024.06) was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Fig. 1 in Effect of soil moisture on Plectris aliena (Coleoptera: Scarabaeidae) oviposition
Fig. 1. Mean numbers of eggs laid by Plectris aliena in laboratory cages with soil moisture levels of 2%, 11%, and 20%. Means with the same letter are not significantly different from one another (Tukey's multiple comparison test, 5% level). Vertical bars are standard errors of the means.
Data, plotting scripts, and figures for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures"
<p>This bundle of files contains all the data and plotting scripts for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures", as well as the figures themselves.</p> <p>These results are part of the paper:</p> <p>Tejas Chandrashekhar Mulky and Kyle E. Niemeyer. "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures," 2018. Accepted for publication in <em>Proceedings of the Combustion Institute</em>, available via <a href="https://arxiv.org/abs/1806.08396">https://arxiv.org/abs/1806.08396</a></p>
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