Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available.This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Web Map that contains over 30 data layers relevant to agricultural development in Ghana. Physical (soils, temperature, & rainfall), demographic (population, population density & unemployment rate), and agricultural (market location, irrigation sites, & farm locations) variables are included.

Irrigation is the provision of water to crops beyond what is provided by local rainfall. Irrigation is a vital part of agriculture in certain areas of Canada like the southern Prairies and the interior of British Columbia. The amount of water that needs to be withdrawn for irrigation varies annually. It depends on winter precipitation, and weather and soil moisture during the growing season. Irrigation can have both positive and negative effects on the environment.

The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the one stop source for environmental and natural resource data, available anytime, from anywhere. It allows a user to choose an area of interest, browse and select data, customize the format, then download or have it shipped on media. The map layers include data on: Public Land Survey System (PLSS), Census data, demographic statistics, precipitation, temperature, disaster events, conservation easements, elevation, geographic names, geology, government units, hydrography, hydrologic units, land use and land cover, map indexes, ortho imagery, soils, topographic images, and streets and roads. This service is made available through a close partnership between the three Service Center Agencies (SCA): Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA), and Rural Development (RD). Resources in this dataset:Resource Title: Geospatial Data Gateway. File Name: Web Page, url: https://gdg.sc.egov.usda.gov This is the main page for the GDG that includes several links to view, download, or order various datasets. Find additional status maps that indicate the location of data available for each map layer in the Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGHome_StatusMaps.aspx

The ARS Water Data Base is a collection of precipitation and streamflow data from small agricultural watersheds in the United States. This national archive of variable time-series readings for precipitation and runoff contains sufficient detail to reconstruct storm hydrographs and hyetographs. There are currently about 14,000 station years of data stored in the data base. Watersheds used as study areas range from 0.2 hectare (0.5 acres) to 12,400 square kilometers (4,786 square miles). Raingage networks range from one station per watershed to over 200 stations. The period of record for individual watersheds vary from 1 to 50 years. Some watersheds have been in continuous operation since the mid 1930's. Resources in this dataset:Resource Title: FORMAT INFORMATION FOR VARIOUS RECORD TYPES. File Name: format.txtResource Description: Format information identifying fields and their length will be included in this file for all files except those ending with the extension .txt TYPES OF FILES As indicated in the previous section data has been stored by _location number in the form, LXX where XX is the _location number. In each subdirectory, there will be various files using the following naming conventions: Runoff data: WSXXX.zip where XXX is the watershed number assigned by the WDC. This number may or may not correspond to a naming convention used in common literature. Rainfall data: RGXXXXXX.zip where XXXXXX is the rain gage station identification. Maximum-minimum daily air temperature: MMTXXXXX.zip where XXXXX is the watershed number assigned by the WDC. Ancillary text files: NOTXXXXX.txt where XXXXX is the watershed number assigned by the WDC. These files will contain textual information including latitude-longitude, name commonly used in literature, acreage, most commonly-associated rain gage(s) (if known by the WDC), a list of all rain gages on or near the watershed. Land use, topography, and soils as known by the WDC. Topographic maps of the watersheds: MAPXXXXX.zip where XXXXX is the _location/watershed number assigned by the WDC. Map files are binary TIF files. NOT ALL FILE TYPES MAY BE AVAILABLE FOR SPECIFIC WATERSHEDS. Data files are still being compiled and translated into a form viable for this archive. Please bear with us while we grow.Resource Title: Data Inventory - watersheds. File Name: inventor.txtResource Description: Watersheds at which records of runoff were being collected by the Agricultural Research Service. Variables: Study Location & Number of Rain Gages1; Name; Lat.; Long; Number; Pub. Code; Record Began; Land Use2; Area (Acres); Types of Data3Resource Title: Information about the ARS Water Database. File Name: README.txtResource Title: INDEX TO INFORMATION ON EXPERIMENTAL AGRICULTURAL WATERSHEDS. File Name: INDEX.TXTResource Description: This report includes identification information on all watersheds operated by the ARS. Only some of these are included in the ARS Water Data Base. They are so indicated in the column titled ARS Water Data Base. Other watersheds will not have data available here or through the Water Data Center. This index is particularly important since it relates watershed names with the indexing system used by the Water Data Center. Each _location has been assigned a number. The data for that _location will be stored in a sub-directory coded as LXX where XX is the _location number. The index also indicates the watershed number used by the WDC. Data for a particular watershed will be stored in a compressed file named WSXXXXX.zip where XXXXX is the watershed number assigned by the WDC. Although not included in the index, rain gage information will be stored in compressed files named RGXXXXXX.zip where XXXXXX is a 6-character identification of the rain gage station. The Index also provides information such as latitude-longitude for each of the watersheds, acreage, the period-of-record for each acreage. Multiple entries for a particular watershed will either indicate that the acreage designated for the watershed changed or there was a break in operations of the watershed. Resource Title: ARS Water Database files. File Name: ars_water.zipResource Description: USING THIS SYSTEM Before downloading huge amounts of data from the ARS Water Data Base, you should first review the text files included in this directory. They include: INDEX OF ARS EXPERIMENTAL WATERSHEDS: index.txt This report includes identification information on all watersheds operated by the ARS. Only some of these are included in the ARS Water Data Base. They are so indicated in the column titled ARS Water Data Base. Other watersheds will not have data available here or through the Water Data Center. This index is particularly important since it relates watershed names with the indexing system used by the Water Data Center. Each _location has been assigned a number. The data for that _location will be stored in a sub-directory coded as LXX where XX is the _location number. The index also indicates the watershed number used by the WDC. Data for a particular watershed will be stored in a compressed file named WSXXXXX.zip where XXXXX is the watershed number assigned by the WDC. Although not included in the index, rain gage information will be stored in compressed files named RGXXXXXX.zip where XXXXXX is a 6-character identification of the rain gage station. The Index also provides information such as latitude-longitude for each of the watersheds, acreage, the period-of-record for each acreage. Multiple entries for a particular watershed will either indicate that the acreage designated for the watershed changed or there was a break in operations of the watershed. STATION TABLE FOR THE ARS WATER DATA BASE: station.txt This report indicates the period of record for each recording station represented in the ARS Water Data Base. The data for a particular station will be stored in a single compressed file. FORMAT INFORMATION FOR VARIOUS RECORD TYPES: format.txt Format information identifying fields and their length will be included in this file for all files except those ending with the extension .txt TYPES OF FILES As indicated in the previous section data has been stored by _location number in the form, LXX where XX is the _location number. In each subdirectory, there will be various files using the following naming conventions: Runoff data: WSXXX.zip where XXX is the watershed number assigned by the WDC. This number may or may not correspond to a naming convention used in common literature. Rainfall data: RGXXXXXX.zip where XXXXXX is the rain gage station identification. Maximum-minimum daily air temperature: MMTXXXXX.zip where XXXXX is the watershed number assigned by the WDC. Ancillary text files: NOTXXXXX.txt where XXXXX is the watershed number assigned by the WDC. These files will contain textual information including latitude-longitude, name commonly used in literature, acreage, most commonly-associated rain gage(s) (if known by the WDC), a list of all rain gages on or near the watershed. Land use, topography, and soils as known by the WDC. Topographic maps of the watersheds: MAPXXXXX.zip where XXXXX is the _location/watershed number assigned by the WDC. Map files are binary TIF files. NOT ALL FILE TYPES MAY BE AVAILABLE FOR SPECIFIC WATERSHEDS. Data files are still being compiled and translated into a form viable for this archive. Please bear with us while we grow.

The GEOECOLOGY Data Base is a compilation of environmental data for research and development needs that includes information for the period 1964 to 1979 (Olson et al. 1980). The data have been used for environmental assessment and planning for energy development requiring rapid access to data at appropriate spatial and temporal scales (Kitchings et al. 1976, Klopatek et al. 1979 a and b, Olson et al. 1982 and 1983). The GEOECOLOGY Data Base contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level of resolution. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.The integrated database of diverse environmental resource information from extant sources was developed by the Environmental Sciences Division at Oak Ridge National Laboratory (Olson et al. 1980). The GEOECOLOGY Data Base can be accessed by downloading the individual files as tab-delimited .txt files. There are 66 individual data sets listed below that available in the collection. Some data sets described in Olson et al. 1980 that were initially available are not included. County data sets have Federal Information Processing Standards (FIPS) numeric state and county codes that allows merging of the separate files. Some files, such as species dictionaries or point data, do not have FIPS identifiers. Data are stored in metric-SI units. The GEOECOLOGY technical memorandum (Olson et al. 1980) is provided as a .pdf file to serve as both documentation and a user's guide to the GEOECOLOGY Data Base giving general information on the data set contents, source of data, and specific variable descriptions.

Rainfall in India (1901–2015)

Dataset Overview

This dataset provides comprehensive monthly, seasonal, and annual rainfall statistics across India's 36 meteorological subdivisions from 1901 to 2015. It offers a detailed view into India's historical rainfall patterns, making it highly valuable for climate research, time-series forecasting, and environmental studies.

Columns Description

Data Summary

Total Rows: 4,116 Total Columns: 19 Years Covered: 1901–2015 Regions: 36 subdivisions across India Data Types: Categorical: SUBDIVISION Numerical: Monthly and seasonal rainfall (in millimeters)

Missing Values: Some minor missing values across a few months and aggregated columns (mostly very few compared to the dataset size).

Sample Records

Potential Use Cases

📈 Trend Analysis: Study long-term changes in rainfall patterns due to climate change. 🌧 Monsoon Research: Analyze the strength and timing of monsoon seasons across different regions. 🌍 Environmental Studies: Explore the relationship between rainfall and agricultural or ecological changes. 🤖 Predictive Modeling: Build models to forecast future rainfall or detect extreme weather events. 🛰 Geospatial Analysis: Visualize and map rainfall trends across India's diverse subdivisions.

Acknowledgements

This dataset is a rich resource for researchers, meteorologists, data scientists, and students working in the areas of climatology, environment, and machine learning.

The Emissions Reduction Fund (ERF) creates a financial incentive for Australian businesses to adopt smarter practices to reduce emissions of greenhouse gases. Participants can earn carbon credits by …Show full descriptionThe Emissions Reduction Fund (ERF) creates a financial incentive for Australian businesses to adopt smarter practices to reduce emissions of greenhouse gases. Participants can earn carbon credits by undertaking a project using an approved ERF method, which sets out the rules for the activity. These methods ensure that emissions reductions are genuine - that they are both real and additional to business as usual operations. ERF environmental data layers are provided to assist proponents’ participation in the ERF. This page provides access to data layers that can be used under a number of agricultural and vegetation methods to assist proponents’ implementation of these land sector methods. Forest Cover - Preview dataset (WMS) You can view all the National forest and sparse woody forest cover data on National Map or your GIS program; Forest cover data (Version 3, 2018). Forest cover data This links to the National forest and sparse woody forest cover data so you can download individual tiles; Forest cover data (Version 3, 2018). ERF Environmental Data - Preview dataset (WMS) You can view the following data on National Map or your GIS. Reforestation by Environmental or Mallee Plantings – the five spatial regions; CFI Mapping Tool - Long term average annual rainfall; and Estimating soil carbon sequestration using default values. Long term average rainfall map layer - CFI rainfall map This links to data showing the long-term average annual rainfall (mm) across continental Australia, calculated for the period 1921-2010. Estimating soil carbon sequestration using default values This links to data showing default soil carbon sequestration rates for projects using the Carbon Credits (Carbon Farming Initiative—Estimating Sequestration of Carbon in Soil Using Default Values) Methodology Determination 2015. Reforestation by Environmental or Mallee Plantings This links to data defining five spatial regions applicable to Carbon Credits (Carbon Farming Initiative) (Reforestation by Environmental or Mallee Plantings (FullCAM)) Methodology Determination 2014. Site potential (M) and Forest Productivity Index (FPIavg) versions 2.0 This links to version 2.0 of the spatial layers for site potential (M) and Long-Term Average Forest Productivity Index (FPIavg). Site potential (M), ratio and Forest Productivity Index (FPIavg) versions 1.0 This links to version 1.0 of the spatial layers for site potential (M), site potential ratio and Long-Term Average Forest Productivity Index (FPIavg). Specified regions for subregulation 20AB(5) This dataset can be found at https://data.gov.au/data/dataset/emissions-reduction-fund-environmental-data-2023

This web mapping application shows the monitoring networks used to track drought conditions across Manitoba. Each tab displays a different source of data, including: streamflow and water level, groundwater, precipitation, reservoir supply status, and Canadian and United States Drought Monitor contours. Each of the data sources are explained in more detail below. Please note the following information when using the web mapping application: When you click on a data point on the River and Lake, Groundwater or Reservoir maps, a pop-up box will appear. This pop-up box contains information on the streamflow (in cubic feet per second; ft3/s), water level (in feet), groundwater level (in metres), storage volume (acre-feet), or supply status (in per cent of full supply level; %) for that location. Click on the Percentile Plot link at the bottom of the pop-up box to view a three-year time series of observed conditions (available for river and lake and groundwater conditions only). A toolbar is located in the top right corner of the web mapping application. The Query Tool can be used to search for a specific river, lake or reservoir monitoring station by name or aquifer type by location. The Layer List enables the user to toggle between precipitation conditions layers (1-month, 3-month, and 12-month) and increase or decrease the transparency of the layer. Data is current for the date indicated on the pop-up box, percentile plot, or map product. Near-real time data are preliminary and subject to change upon review. River and lake conditions are monitored to determine the severity of hydrological dryness in a watershed. River and lake measurements are converted to percentiles by comparing daily measurements from a specified day to historical measurements over the monitoring station’s period of record for that particular day. A percentile is a value on a scale of zero to 100 that indicates the percent of a distribution that is equal to or below it. In general: Streamflow (or lake level) which is greater than the 90th percentile is classified as “much above normal”. Streamflow (or lake level) which is between the 75th and 90th percentile is classified as “above normal”. Streamflow (or lake level) which is between the 25th and 75th percentiles is classified as “normal”. Streamflow (or lake level) which is between the 10th and 25th percentile is classified as “below normal”. Streamflow (or lake level) which is less than the 10th percentile is classified as “much below normal”. "Median" indicates the midpoint (or 50th percentile) of the distribution, whereby 50 per cent of the data falls below the given point, and 50 per cent falls above. Other flow categories include: "Lowest" indicates that the estimated streamflow (or lake level) is the lowest value ever measured for the day of the year. "Highest" indicates that the estimated streamflow (or lake level) is the highest value ever measured for the day of the year. Monitoring stations classified as “No Data” do not have current estimates of streamflow (or lake level) available. Click on the Percentile Plot link at the bottom of the pop-up box to view a graph (in PDF format) displaying a three-year time series of observed conditions relative to the historical percentiles described above. The period of record used to compute the percentiles is stated, alongside the station ID, and if the river or lake is regulated (i.e. controlled) or natural. Hydrometric data are obtained from Water Survey of Canada, Manitoba Infrastructure, and the United States Geological Survey. Near real-time data are preliminary as they can be impacted by ice, wind, or equipment malfunction. Preliminary data are subject to change upon review. Groundwater conditions are monitored to determine the severity of hydrological dryness in an aquifer. Water levels are converted to percentiles by comparing daily measurements from a specified day to historical measurements over the monitoring station’s period of record for that particular day. A percentile is a value on a scale of zero to 100 that indicates the percent of a distribution that is equal to or below it. In general: A groundwater level which is greater than the 90th percentile is classified as “much above normal”. A groundwater level which is between the 75th and 90th percentile is classified as “above normal”. A groundwater level which is between the 25th and 75th percentiles is classified as “normal”. A groundwater level which is between the 10th and 25th percentile is classified as “below normal”. A groundwater level which is less than the 10th percentile is classified as “much below normal”. Monitoring stations classified as “No Data” do not have current measurements of groundwater level available. "Median" indicates the midpoint (or 50th percentile) of the distribution, whereby 50 per cent of the data falls below the given point, and 50 per cent falls above. Click on the Percentile Plot link at the bottom of the pop-up box to view a graph (in PDF format) displaying a three-year time series of observed conditions relative to the historical percentiles described above. The period of record used to compute the percentiles is stated, alongside the station ID. Precipitation conditions maps are developed to determine the severity of meteorological dryness and are also an indirect measurement of agricultural dryness. Precipitation indicators are calculated at over 40 locations by comparing total precipitation over the time period to long-term (1971 – 2015) medians. Three different time periods are used to represent: (1) short-term conditions (the past month), (2) medium-term conditions (the past three months), and (3) long-term conditions (the past twelve months). These indicator values are then interpolated across the province to produce the maps provided here. Long-term and medium-term precipitation indicators provide the most appropriate assessment of dryness as the short term indicator is influenced by significant rainfall events and spatial variability in rainfall, particularly during summer storms. Due to large distances between meteorological stations in northern Manitoba, the interpolated contours in this region are based on limited observations and should be interpreted with caution. Precipitation conditions are classified as follows: Per cent of median greater than 115 per cent is classified as “above normal”. Per cent of median between 85 per cent and 115 per cent is classified as “normal”. Per cent of median between 60 per cent and 85 per cent is classified as “moderately dry”. Per cent of median between 40 per cent and 60 per cent is classified as a “severely dry”. Per cent of median less than 40 per cent is classified as an “extremely dry”. Precipitation data is obtained from Environment and Climate Change Canada, Manitoba Agriculture, and Manitoba Sustainable Development’s Fire Program. Reservoir conditions are monitored at 15 locations across southern Manitoba to track water availability, including possible water shortages. Conditions are reported both as a water level and as a “supply status”. The supply status is the current amount of water stored in the reservoir compared to the target storage volume of the reservoir (termed “full supply level”). A supply status greater than 100 per cent represents a reservoir that is exceeding full supply level. Canadian and U.S Drought Monitors: Several governments, agencies, and universities monitor the spatial extent and intensity of drought conditions across Canada and the United States, producing maps and data products available through the Canadian Drought Monitor and United States Drought Monitor websites. The Canadian Drought Monitor is managed through Agriculture and Agri-Food Canada, while the United States Drought Monitor is a joint effort between The National Drought Mitigation Centre (at the University of Nebraska-Lincoln), the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. The drought monitor assessments are based on a suite of drought indicators, impacts data and local reports as interpreted by federal, provincial/state and academic scientists. Both the Canadian and United States drought assessments have been amalgamated to form this map, and use the following drought classification system: D0 (Abnormally Dry) – represents an event that occurs every 3 - 5 years; D1 (Moderate Drought) – 5 to 10 year event; D2 (Severe Drought) – 10 to 20 year event; D3 (Extreme Drought) – 20 to 50 year event; and D4 (Exceptional Drought) – 50+ year event. Additionally, the map indicates whether drought impacts are: (1) short-term (S); typically less than six months, such as impacts to agriculture and grasslands, (2) long-term (L); typically more than six months, such as impacts to hydrology and ecology, or (3) a combination of both short-term and long-term impacts (SL). The Canadian Drought Monitor publishes its assessments monthly, and United States Drought Monitor maps are released weekly on Thursday mornings. The amalgamated map provided here will be updated on a monthly basis corresponding to the release of the Canadian Drought Monitor map. Care will be taken to ensure both maps highlight drought conditions for the same point in time; however the assessment dates may differ between Canada and the United States due to when the maps are published. Please click on an area of drought on the map to confirm the assessment date. Canadian Drought Monitor data are subject to the Government of Canada Open Data Licence Agreement: https://open.canada.ca/en/open-government-licence-canada. United States Drought Monitor data are available on the United States Drought Monitor website: https://droughtmonitor.unl.edu. For more information, please visit the Manitoba Drought Monitor website.

"The Great Plains of the United States is characterized by a large west-east gradient in annual precipitation and a similar large north-south gradient in annual temperature. Native grasslands and winter wheat are found over a large portion of the precipitation and temperature gradients. In this article, we use long-term data to analyze the differences in the patterns in aboveground net primary production and precipitation-use efficiency between wheat and native grassland ecosystems in the central portion of Great Plains, and their relationships to potential water availability (precipitation). Aboveground net primary production of native grasslands shows a large response to precipitation. Aboveground net primary production of winter wheat has a smaller response to changing precipitation. Annual precipitation-use efficiency of native grasslands is unaffected by increases in average annual precipitation, but precipitation-use efficiency of summer-fallow wheat ecosystems decreases substantially with increased average precipitation. Our results suggest that in the wetter portion of the central Great Plains, summer-fallow wheat management is relatively inefficient, because increased water availability results in diminishing returns. Comparisons with data from continuously cropped wheat confirmed this result. Shifts across the region to continuous cropping of wheat potentially could have significant impacts on regional wheat yield, carbon balance, and economic status". To read the full article, access http://link.springer.de/link/service/journals/10021 to obtain information about how to become qualified to read this article.

Rain is vital for life – it supplies the water we need to drink and to grow our food, keeps our ecosystems healthy, and supplies our electricity. New Zealand’s mountainous terrain and location in the roaring forties mean rainfall varies across the country. Changes in rainfall amount or timing can significantly affect agriculture, energy, recreation, and the environment. For example, an increase or decrease of rainfall in spring can have marked effects on crops or fish populations.
More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

The Africa Soil Information Service (AfSIS) Climate Collection's WorldClim data set contains rasters with the following calculations: time series average for BIO1 temperature as well as time series average and time series Modified Fournier Index (MFI) for BIO12 precipitation. These Africa continent-wide calculations use the temperature and precipitation data for the period 1950-2000 created by WorldClim. The rasters contain interpolated weather station data with a spatial resolution of 1 kilometer, and are updated by AfSIS using data provided by WorldClim at http://www.worldclim.org.

The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS).

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Important Note: 14/01/2015. Since we generated these spatial layer datasets, the NSW Department of Planning and Environment has published an interactive CSG Exclusion Zone map. Interested parties should go to http://www.planning.nsw.gov.au/en-au/planningyourregion/strategicregionallanduse/coalseamgasexclusionzones.aspx where they can find out more about CSG exclusion zones. The information in the Bioregional Assessment products aligns with the CSG exclusion zones as published by NSW and the subsequent publication of those NSW maps does not alter the information in our assessments.

A polygon dataset that estimates the extent of Strategic Agricultural Land (SAL) within New South Wales.

Strategic agricultural land is highly productive land that has both unique natural resource characteristics (such as soil and water resources) as well as socio-economic value (such as high productivity, infrastructure availability and access to markets).

Biophysical strategic agricultural land is land with a rare combination of natural resources highly suitable for agriculture. These lands intrinsically have the best quality landforms, soil and water resources which are naturally capable of sustaining high levels of productivity and require minimal management practices to maintain this high quality

Purpose

To identify Strategic Agricultural Land (SAL) within the state

Dataset History

Important Note: 14/01/2015. Since we generated these spatial layer datasets, the NSW Department of Planning and Environment has published an interactive CSG Exclusion Zone map. Interested parties should go to http://www.planning.nsw.gov.au/en-au/planningyourregion/strategicregionallanduse/coalseamgasexclusionzones.aspx where they can find out more about CSG exclusion zones. The information in the Bioregional Assessment products aligns with the CSG exclusion zones as published by NSW and the subsequent publication of those NSW maps does not alter the information in our assessments.

This dataset has been captured and mapped at a regional scale

Criteria for Biophysical Strategic Agricultural Land

land that falls under soil fertility classes 'high' or 'moderately high' under the Draft Inherent General Fertility of NSW (OEH), and

· land capability classes I, II or III under the Land and Soil Capability Mapping of NSW (OEH), and

· reliable water of suitable quality, characterised by having rainfall of 350mm or more per annum (9 out of 10 years); or properties within 150m of a regulated river, or unregulated rivers where there are flows for at least 95% of the time (ie the 95th percentile flow of each month of the year is greater than zero) or 5th order and higher rivers; or groundwater aquifers (excluding miscellaneous alluvial aquifers, also known as small storage aquifers) which have a yield rate greater than 5L/s and total dissolved solids of less than 1,500mg/L.

OR

· land that falls under soil fertility classes 'moderate' under the Draft Inherent General Fertility of NSW (OEH), and

· land capability classes I or II under the Land and Soil Capability Mapping of NSW (OEH), and

· reliable water of suitable quality, characterised by having rainfall of 350mm or more per annum (9 out of 10 years); or properties within 150m of a regulated river, or unregulated rivers where there are flows for at least 95% of the time (ie the 95th percentile flow of each month of the year is greater than zero) or 5th order and higher rivers; or groundwater aquifers (excluding miscellaneous alluvial aquifers, also known as small storage aquifers) which have a yield rate greater than 5L/s and total dissolved solids of less than 1,500mg/L.

Dataset Citation

NSW Department of Planning and Infrastructure (2013) Strategic Agricultural Lands (SAL) Biophysical. Bioregional Assessment Source Dataset. Viewed 14 June 2018, http://data.bioregionalassessments.gov.au/dataset/42e2a51d-3c11-431f-ac62-f8511c859516.

Mean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, …Show full descriptionMean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, reflecting the effects of water limitation in most areas of the continent. Both the annual mean and seasonal pattern broadly resemble the corresponding patterns for rainfall. Canopy transpiration maps have more acontrasta (relative variation) than the total evaporation or rainfall maps because transpiration is a small component of total evaporation in areas of low plant cover (the remainder being soil evaporation).The partitioning between soil evaporation and canopy transpiration in the model is determined by leaf area index.Notes: These are model-based estimates from the BiosEquil model. Results are given for the aBASEa (pre-1788) and aAGRICa (present day) conditions. The aAGRICa case includes current agricultural inputs of water from irrigation.EvapCanopy.00.Agric, EvapCanopy.01.Agric a| EvapCanopy.12.Agric a Mean annual and monthly canopy transpiration (mm) in the aAGRICa (present day) scenarioEvapCanopy.00.Base, EvapCanopy.01.Base a| EvapCanopy.12.Base a Mean annual and monthly canopy transpiration (mm) in the aBASEa (pre-1788) scenarioData is in geographics and GDA94. See further metadata for more detail.

Mean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, reflecting the effects of water limitation in most areas of the continent. Both the annual mean and seasonal pattern broadly resemble the corresponding patterns for rainfall. Canopy transpiration maps have more acontrasta (relative variation) than the total evaporation or rainfall maps because transpiration is a small component of total evaporation in areas of low plant cover (the remainder being soil evaporation).The partitioning between soil evaporation and canopy transpiration in the model is determined by leaf area index.Notes: These are model-based estimates from the BiosEquil model. Results are given for the aBASEa (pre-1788) and aAGRICa (present day) conditions. The aAGRICa case includes current agricultural inputs of water from irrigation.EvapCanopy.00.Agric, EvapCanopy.01.Agric a| EvapCanopy.12.Agric a Mean annual and monthly canopy transpiration (mm) in the aAGRICa (present day) scenarioEvapCanopy.00.Base, EvapCanopy.01.Base a| EvapCanopy.12.Base a Mean annual and monthly canopy transpiration (mm) in the aBASEa (pre-1788) scenarioData is in geographics and GDA94.

See further metadata for more detail.

Plant communities and fungi inhabiting their phyllospheres change along precipitation gradients and often respond to changes in land use. Many studies have focused on the changes in foliar fungal communities on specific plant species, however, few have addressed the association between whole plant communities and their phyllosphere fungi. We sampled plant communities and associated phyllosphere fungal communities in native prairie remnants and post-agricultural sites across the steep precipitation gradient in the central plains in Kansas, USA. Plant community cover data and MiSeq ITS2 metabarcode data of the phyllosphere fungal communities indicated that both plant and fungal community composition respond strongly to mean annual precipitation (MAP), but less so to land use (native prairie remnants vs. post-agricultural sites). However, plant and fungal diversity were greater in the native remnant prairies than in post-agricultural sites. Overall, both plant and fungal diversity increased with MAP and the communities in the arid and mesic parts of the gradient were distinct. Analyses of the linkages between plant and fungal communities (Mantel and Procrustes tests) identified strong correlations between the composition of the two. However, despite the strong correlations, regression models with plant richness, diversity, or composition (ordination axis scores) and land use as explanatory variables for fungal diversity and evenness did not improve the models compared to those with precipitation and land use (ΔAIC < 2), even though the explanatory power of some plant variables was greater than that of MAP as measured by R2. Indicator taxon analyses suggest that grass species are the primary taxa that differ in the plant communities. Similar analyses of the phyllosphere fungi indicated that many plant pathogens are disproportionately abundant either in the arid or mesic environments. Although decoupling the drivers of fungal communities and their composition – whether abiotic or host-dependent – remains a challenge, our study highlights the distinct community responses to precipitation and the tight tracking of the plant communities by their associated fungal symbionts.

Mean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, reflecting the effects of water limitation in most areas of the continent. Both the annual mean and seasonal pattern broadly resemble the corresponding patterns for rainfall. Canopy transpiration maps have more acontrasta (relative variation) than the total evaporation or rainfall maps because transpiration is a small component of total evaporation in areas of low plant cover (the remainder being soil evaporation).The partitioning between soil evaporation and canopy transpiration in the model is determined by leaf area index.Notes: These are model-based estimates from the BiosEquil model. Results are given for the aBASEa (pre-1788) and aAGRICa (present day) conditions. The aAGRICa case includes current agricultural inputs of water from irrigation.EvapCanopy.00.Agric, EvapCanopy.01.Agric a| EvapCanopy.12.Agric a Mean annual and monthly canopy transpiration (mm) in the aAGRICa (present day) scenarioEvapCanopy.00.Base, EvapCanopy.01.Base a| EvapCanopy.12.Base a Mean annual and monthly canopy transpiration (mm) in the aBASEa (pre-1788) scenarioData is in geographics and GDA94.

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Mean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, …Show full descriptionMean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, reflecting the effects of water limitation in most areas of the continent. Both the annual mean and seasonal pattern broadly resemble the corresponding patterns for rainfall. Canopy transpiration maps have more acontrasta (relative variation) than the total evaporation or rainfall maps because transpiration is a small component of total evaporation in areas of low plant cover (the remainder being soil evaporation).The partitioning between soil evaporation and canopy transpiration in the model is determined by leaf area index.Notes: These are model-based estimates from the BiosEquil model. Results are given for the aBASEa (pre-1788) and aAGRICa (present day) conditions. The aAGRICa case includes current agricultural inputs of water from irrigation.EvapCanopy.00.Agric, EvapCanopy.01.Agric a| EvapCanopy.12.Agric a Mean annual and monthly canopy transpiration (mm) in the aAGRICa (present day) scenarioEvapCanopy.00.Base, EvapCanopy.01.Base a| EvapCanopy.12.Base a Mean annual and monthly canopy transpiration (mm) in the aBASEa (pre-1788) scenarioData is in geographics and GDA94. See further metadata for more detail.

Mean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, reflecting the effects of water limitation in most areas of the continent. Both the annual mean and seasonal pattern broadly resemble the corresponding patterns for rainfall. Canopy transpiration maps have more acontrasta (relative variation) than the total evaporation or rainfall maps because transpiration is a small component of total evaporation in areas of low plant cover (the remainder being soil evaporation).The partitioning between soil evaporation and canopy transpiration in the model is determined by leaf area index.Notes: These are model-based estimates from the BiosEquil model. Results are given for the aBASEa (pre-1788) and aAGRICa (present day) conditions. The aAGRICa case includes current agricultural inputs of water from irrigation.EvapCanopy.00.Agric, EvapCanopy.01.Agric a| EvapCanopy.12.Agric a Mean annual and monthly canopy transpiration (mm) in the aAGRICa (present day) scenarioEvapCanopy.00.Base, EvapCanopy.01.Base a| EvapCanopy.12.Base a Mean annual and monthly canopy transpiration (mm) in the aBASEa (pre-1788) scenarioData is in geographics and GDA94.

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Mean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, …Show full descriptionMean annual (00) and monthly (01a|12) transpiration (mm) from the plant canopy for the period 1980-1999.Interpretation:Canopy transpiration is far more spatially variable than potential evaporation, reflecting the effects of water limitation in most areas of the continent. Both the annual mean and seasonal pattern broadly resemble the corresponding patterns for rainfall. Canopy transpiration maps have more acontrasta (relative variation) than the total evaporation or rainfall maps because transpiration is a small component of total evaporation in areas of low plant cover (the remainder being soil evaporation).The partitioning between soil evaporation and canopy transpiration in the model is determined by leaf area index.Notes: These are model-based estimates from the BiosEquil model. Results are given for the aBASEa (pre-1788) and aAGRICa (present day) conditions. The aAGRICa case includes current agricultural inputs of water from irrigation.EvapCanopy.00.Agric, EvapCanopy.01.Agric a| EvapCanopy.12.Agric a Mean annual and monthly canopy transpiration (mm) in the aAGRICa (present day) scenarioEvapCanopy.00.Base, EvapCanopy.01.Base a| EvapCanopy.12.Base a Mean annual and monthly canopy transpiration (mm) in the aBASEa (pre-1788) scenarioData is in geographics and GDA94. See further metadata for more detail.