Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280.
Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. Resources in this dataset:Resource Title: Cattle weight gain. File Name: Cattle_weight_gains.xlsxResource Description: Cattle weight gain data for Grass-Cast Database. Resource Title: NDVI. File Name: NDVI.xlsxResource Description: Annual NDVI growing season values for Grass-Cast sites. See readme for more information and NDVI_raw for the raw values. Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated. Resource Title: Grass-Cast R script . File Name: R_access_script.zipResource Description: R script (in Rmarkdown [Rmd] format) for uploading and looking at Grass-Cast data.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

The spearfish sample database is being distributed to provide users with a solid database on which to work for learning the tools of GRASS. This document provides some general information about the database and the map layers available. With the release of GRASS 4.1, the GRASS development staff is pleased to announce that the sample data set spearfish is also being distributed. The spearfish data set covers two topographic 1:24,000 quads in western South Dakota. The names of the quads are Spearfish and Deadwood North, SD. The area covered by the data set is in the vicinity of Spearfish, SD and includes a majority of the Black Hills National Forest (i.e., Mount Rushmore). It is anticipated that enough data layers will be provided to allow users to use nearly all of the GRASS tools on the spearfish data set. A majority of this spearfish database was initially provided to USACERL by the EROS Data Center (EDC) in Sioux Falls, SD. The GRASS Development staff expresses acknowledgement and thanks to: the U.S. Geological Survey (USGS) and EROS Data Center for allowing us to distribute this data with our release of GRASS software; and to the U.S. Census Bureau for their samples of TIGER/Line data and the STF1 data which were used in the development of the TIGER programs and tutorials. Thanks also to SPOT Image Corporation for providing multispectral and panchromatic satellite imagery for a portion of the spearfish data set and for allowing us to distribute this imagery with GRASS software. In addition to the data provided by the EDC and SPOT, researchers at USACERL have dev eloped several new layers, thus enhancing the spearfish data set. To use the spearfish data, when entering GRASS, enter spearfish as your choice for the current location.

This is the classical GRASS GIS dataset from 1993 covering a part of Spearfish, South Dakota, USA, with raster, vector and point data. The Spearfish data base covers two 7.5 minute topographic sheets in the northern Black Hills of South Dakota, USA. It is in the Universal Transverse Mercator Projection. It was originally created by Larry Batten while he was with the U. S. Geological Survey's EROS Data Center in South Dakota. The data base was enhanced by USA/CERL and cooperators.

Invasive annual grasses (IAGs) present a persistent challenge for the ecological management of rangelands, particularly the imperiled sagebrush biome in western North America. Cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and Ventenata spp. are spreading across sagebrush rangelands and already occupy at least 200,000 kilometers squared (km sq.) of the intermountain west. The loss and degradation of native plant communities caused by IAGs threatens the persistence of sagebrush obligate species such as the Greater Sage-grouse (Centrocercus urophasianus) and pygmy rabbit (Brachylagus idahoensis). IAGs convert sagebrush landscapes to monocultures of non-native grasslands that substantially increase the risk of wildfire and degrade important ecosystem services including forage production and quality, soil stability, and carbon sequestration. As a result, the economic consequences of IAGs are substantial. Successful management of IAG invasions depends on extensi ...

Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated.

10780 Global import shipment records of Grass Seed with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Sichuan Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for Sichuan Province, China. The top level directory sichuan-dataset is a GRASS GIS location for WGS 84 / UTM zone 48N with EPSG code 32648. Inside the location there is the PERMANENT mapset, color tables, category tables, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• ALOS Global Digital Surface Model
• Anthromes
• NASA Shuttle Radar Topography Mission (SRTM)
• ESA CCI Landcover
• European Soil Data Centre (ESDAC)
• Database of Global Administrative Areas (GADM)
• Harvard World Map
• IUCN Red List
• Protected Planet
• NASA Blue Marble
• Natural Earth
• Open Street Map
• WWF HydroSHEDS
• WWF Terrestrial Ecoregions of the World

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

Governor's Island Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for Governor's Island, New York City, USA. The top level directory governors_island_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

Maps

• Orthophotographs from 2012, 2014, 2016, 2018, and 2020
• Digital elevation model from 2017
• Digital surface models from 2014 and 2017
• Landcover from 2014

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

112603 Global export shipment records of Artificial Grass with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Fire can shape plant communities when species respond differently to fire characteristics like season and intensity. If invasive plants are more vulnerable than native species to specific aspects of fire, managers could use prescribed fires to control non-native species. We conducted a meta-analysis of fire effects on six closely related Afro-Eurasian and Australian grasses (Bothriochloa bladhii, B. ischaemum, B. pertusa, Dichanthium annulatum, D. aristatum, and D. sericeum, collectively “invasive bluestems”) that have invaded grasslands worldwide. Using data from 31 studies (only 3 from their native range), we evaluated whether these grasses (275 effect sizes) responded differently than native grasses (184 effect sizes) to fire season, fuel load, and drought. Original data included 15 different response variables (e.g., biomass, survival) that were converted to standardized mean difference for analysis. Fires in summer, fall, and early winter had negative effects on invasive bluestems; no fire season had significant positive effects. Most data were for B. ischaemum, but the other bluestems may also be vulnerable to summer fire. Native grasses did not show significant negative responses in any month. Drought (Keetch-Byram Drought Index) in the month before fire increased the negative effects of fire on invasive bluestems but not native grasses. Drought after fire led to similar negative effects on both groups. Unexpectedly, fuel load (which influences fire intensity) did not significantly influence fire effects in any analysis. At the fuel loads examined (~600 – 10,000 kg/ha dried herbaceous biomass), fire intensity may have been too low to cause meristem mortality. Between-study heterogeneity was large in all analyses (I2>80%), suggesting that additional factors beyond those reported in the studies influence fire effects. These factors could include plant phenology, fire behavior, weather conditions during the fire, and soil characteristics. Synthesis and applications: Fires during summer and fall, especially during dry conditions, could harm invasive bluestems relative to native grasses, likely due to subtle differences in heat sensitivity, phenology, and drought resistance. Other invasive species may have similar vulnerabilities to specific fire seasons and rainfall conditions that allow the use of fire as a control method. Methods We searched Web of Science, Agricola, Proquest, and GoogleScholar on July 15, 2023 (Table S1). A separate search was conducted in each database for each of the six focal species (Bothriochloa bladhii, Bothriochloa ischaemum, Bothriochloa pertusa, Dichanthium annulatum, Dichanthium aristatum, and Dichanthium sericeum). The search term included scientific and common names as well as “fire OR burn*” (Table S2); search terms were the same for each database. After de-duplication, these initial searches identified 272 unique documents (including theses, dissertations, and unpublished reports). Table S1: Details of databases searched

Database

Details

Web of Science

Searched ‘topic’. Web of Science Core Collection included Science Citation Index Expanded (SCI-EXPANDED—1900-present); Social Sciences Citation Index (SSCI)—1900-present; Arts & Humanities Citation Index (AHCI)—1975-present; Conference Proceedings Citation Index—Science (CPCI-S)—1900-present; Conference Proceedings Citation Index—Social Science & Humanities (CPCI-SSH)—1990-present; Book Citation Index—Science (BKCI-S)—2005-present; Book Citation Index—Social Sciences & Humanities (BKCI-SSH)—2005-present; Emerging Sources Citation Index (SCI)—2005-present; Current Chemical Reactions (CCR-EXPANDED)—1985-present; Index Chemicus (IC)—1993-present.

Agricola

1967 to present; searched ‘TX All Text Fields’

ProQuest Dissertations and Theses Global

1974 to present; searched ‘anywhere but full text-NOFT’

GoogleScholar

No date restrictions; used search strings with only scientific names (otherwise the search term was too long). Stopping point was chosen when there were 10 non-relevant search results on a page.

Table S2: Exact search terms used when searching the databases listed in Table 1.

(“Bothriochloa bladhii” OR “Bothriochloa caucasica” OR “Andropogon bladhii” OR “Bothriochloa intermedia” OR “Andropogon intermedius” OR “Andropogon caucasicus” OR “Caucasian bluestem” OR “Australian beard grass” OR “forest-bluegrass” OR “plains bluestem” OR “purple plume grass”) AND (fire OR burn*)

(“Bothriochloa ischaemum” OR “Amphilophis ischaemum” OR “Andropogon ischaemum” OR “Dichanthium ischaemum” OR “King Ranch bluestem” OR “KR bluestem” OR “yellow bluestem” OR “plains bluestem” OR “bearded finger grass” OR “dogstooth grass” OR “Turkestan bluestem”) AND (fire OR burn*)

(“Bothriochloa pertusa” OR “Andropogon pertusus” OR “Holcus pertusus” OR “Indian-bluegrass” OR “pitted beardgrass” OR “hurricane grass” OR “Indian couch grass” OR “Seymour grass” OR “Barbados sourgrass” OR “Antigua hay” OR “sweet pitted grass” OR “silver grass”) AND (fire OR burn*)

(“Dichanthium annulatum” OR “Andropogon annulatus” OR “Andropogon nodosus” OR “Kleberg bluestem” OR “marvel grass” OR “Diaz bluestem” OR “Hindi grass” OR “ringed dichanthium” OR “sheda grass” OR “medio bluestem” OR “jargu grass” OR “Delhi grass” OR “vuda bluegrass” OR “two-flowered golden-beard” OR “Santa Barbara grass”) AND (fire OR burn*)

(“Dichanthium aristatum” OR “Andropogon aristatus” OR “Angleton bluestem” OR “Angelton bluestem” OR “wildergrass”) AND (fire OR burn*)

(“Dichanthium sericeum” OR “Andropogon sericeus” OR “Silky bluestem” OR “Queensland blue grass” OR “silky bluegrass” OR “slender bluegrass” OR “tassel bluegrass”) AND (fire OR burn*)

Search results were stored in Rayyan (Ouzzani et al., 2016) and were screened manually by one of the authors. Papers were retained for further screening if they included prescribed fire or wildfire, at least one of the focal grass species (in the native or introduced range), and a measure of fire effects on the focal grass. Title and abstract screening eliminated 168 records. Remaining records underwent full-text screening with additional criteria: studies included a comparison between burned and unburned treatments; no additional treatments (e.g., fertilizer, mowing) were applied to the measured populations; studies provided information needed for a quantitative meta-analysis (response means, sample size, and measures of variation); and studies provided the month or exact dates of the fires. Where the necessary data were not available in the papers, we attempted to contact the authors. Full-text screening eliminated 76 records. We then conducted a forward/backward citation search based on the papers included after full-text screening, as well as articles that were themselves eliminated because data were combined across treatments (e.g., averaged across fertilizer levels, n = 20), no variance was reported (n = 8), or fire history was not available (n = 2). These searches were conducted in August 2023 and added three additional papers for a final total of 31 papers that met all search criteria. We extracted data from tables, figures (using WebPlotDigitizer 4.6, Rohatgi 2022), published supplemental datasets, or data provided by the authors. We extracted means for burn and control (unburned) treatments along with sample sizes and standard deviation or standard error. Original data types included basal area, biomass, change in cover, change in frequency, cover, crown area, dead crown density, density, frequency, number of plants, number of seed heads, number of tillers, survival, and stem count. We extracted data for the focal grass species as well as any native grass species presented in the same papers. When multiple papers were published about the same study, we used the data from the most recent publication, but included additional data from earlier papers if they presented different information (e.g., cover vs biomass; more detailed treatment groupings), taking care to not duplicate data. We compiled additional information to serve as moderators (defined in Table S3) including site, latitude, species name, species range of bluestems (native vs introduced), photosynthesis type of native grasses (C3 and C4 species according to Cerros-Tlatilpa et al. [2011] and Osborne et al. [2014]), seeding with native species (excluding focal bluestems), current grazing, study type (experimental, observational), fire type (prescribed fire, wildfire, burn box/burn barrel), time since fire, date/month of fire, and response type (e.g., frequency, cover). Because we found more sites in the northern hemisphere, fire months from the southern hemisphere were adjusted by adding 6 months, making them seasonally equivalent to northern hemisphere months (e.g., “July” is always summer). We also recorded fuel load (dried herbaceous biomass) and soil depth when available. To examine the influence of drought on fire effects, we calculated the Keetch-Byram Drought Index (KBDI, Keetch, and Byram 1968; Alexander 1990) before and after each fire. This drought index represents the amount of rainfall needed to return the soil to saturation and changes daily based on temperature and rainfall. Values range from 0 (no moisture deficit) to 800. We used this drought index instead of rainfall because the index takes mean annual rainfall into account, allowing comparisons among regions with different climates. To calculate the index, we downloaded temperature and precipitation data from the closest weather station(s) to each study site from National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (for

Data Description Managed turfgrass is a common component of urban landscapes that is expanding under current land use trends. Previous studies have reported high rates of soil carbon sequestration in turfgrass, but no systematic review has summarized these rates nor evaluated how they change as turfgrass ages. We conducted a meta-analysis of soil carbon sequestration rates from 63 studies. Those data, as well as the code used to analyze them and create figures, are shared here. Dataset Development We conducted a systematic review from Nov 2020 to Jan 2021 using Google Scholar, Web of Science, and the Michigan Turfgrass Information File Database. The search terms targeted were "soil carbon", "carbon sequestration", "carbon storage", or “carbon stock”, with "turf", "turfgrass", "lawn", "urban ecosystem", or "residential", “Fescue”, “Zoysia”, “Poa”, “Cynodon”, “Bouteloua”, “Lolium”, or “Agrostis”. We included only peer-reviewed studies written in English that measured SOC change over one year or longer, and where grass was managed as turf (mowed or clipped regularly). We included studies that sampled to any soil depth, and included several methodologies: small-plot research conducted over a few years (22 datasets from 4 articles), chronosequences of golf courses or residential lawns (39 datasets from 16 articles), and one study that was a variation on a chronosequence method and compiled long-term soil test data provided by golf courses of various ages (3 datasets from Qian & Follett, 2002). In total, 63 datasets from 21 articles met the search criteria. We excluded 1) duplicate reports of the same data, 2) small plot studies that did not report baseline SOC stocks, and 3) pure modeling studies. We included five papers that only measured changes in SOC concentrations, but not areal stocks (i.e., SOC in Mg ha-1). For these papers, we converted from concentrations to stocks using several approaches. For two papers (Law & Patton, 2017; Y. Qian & Follett, 2002) we used estimated bulk densities provided by the authors. For the chronosequences reported in Selhorst & Lal (2011), we used the average bulk density reported by the author. For the 13 choronosequences reported in Selhorst & Lal (2013), we estimated bulk density from the average relationship between percent C and bulk density reported by Selhorst (2011). For Wang et al. (2014), we used bulk density values from official soil survey descriptions. Data provenance In most cases we contacted authors of the studies to obtain the original data. If authors did not reply after two inquiries, or no longer had access to the data, we captured data from published figures using WebPlotDigitizer (Rohatgi, 2021). For three manuscripts the data was already available, or partially available, in public data repositories. Data provenance information is provided in the document "Dataset summaries and citations.docx". Recommended Uses We recommend the following to data users:

Consult and cite the original manuscripts for each dataset, which often provide additional information about turfgrass management, experimental methods, and environmental context. Original citations are provided in the document "Dataset summaries and citations.docx". For datasets that were previously published in public repositories, consult and cite the original datasets, which may provide additional data on turfgrass management practices, soil nitrogen, and natural reference sites. Links to repositories are in the document "Dataset summaries and citations.docx". Consider contacting the dataset authors to notify them of your plans to use the data, and to offer co-authorship as appropriate.

The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-yr period, shrub cover decli ...

New Orleans Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for New Orleans, Louisiana, USA. The top level directory new-orleans-dataset is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with EPSG code 3452. Inside the location there are the PERMANENT mapset with citywide data, a vieux_carre mapset with data for the French Quarter, Python scripts for data processing, data records, a color table, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• U.S. Army Corps of Engineers 2012 Lidar Survey of New Orleans
• New Orleans Open Data

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon. The scripts are licensed under the GNU General Public License 3.0 by Brendan Harmon. The graphics are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) by Brendan Harmon.

Get the latest USA Artificial Grass import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

Belgium Cultivated Area: Grass Area data was reported at 474,629.990 ha in 2022. This records a decrease from the previous number of 476,276.790 ha for 2021. Belgium Cultivated Area: Grass Area data is updated yearly, averaging 499,686.530 ha from Apr 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 536,011.490 ha in 2002 and a record low of 467,836.530 ha in 2017. Belgium Cultivated Area: Grass Area data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.B011: Cultivated Area.

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis. - We create two notebooks: 1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input. 2. The second notebook demonstrates the process of model compilation, simulation, and visualization.

• The first notebook includes:

  1. Create Project Directory and Download Raw GIS Data from HydroShare
  2. Set GRASS Database and GISBASE Environment
  3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
  4. Preprocess Time series data for RHESsys Model
  5. Construct worldfile and flowtable to RHESSys

• The second notebook includes:

  1. Download and compile RHESsys Execution file
  2. Simulate RHESsys model
  3. Plotting RHESsys output

Get the latest USA Pampas Grass Artificial import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

112603 Global import shipment records of Artificial Grass with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Comprehensive financial and analytical metrics for Grass, including key performance indicators, market data, and ecosystem analytics.

Comprehensive dataset containing 89 verified Grass locations in Russia with complete contact information, ratings, reviews, and location data.