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Agriculture and Water Resources Data Pathfinder: Water Data

The Colorado River Basin lost nearly 53 million acre feet of freshwater over the past nine years, according to a new study based on data from NASA’s GRACE mission. This is almost double the volume of the nation's largest reservoir, Nevada's Lake Mead (pictured)

The nation's largest reservoir, Nevada's Lake Mead. Image credit: U.S. Bureau of Reclamation

Water is a key component of the overall Earth system, cycling through each component, moving within the atmosphere, the ocean, the cryosphere (including snow cover and snow pack), surface water of rivers and lakes, and subsurface water. Water availability is critical for human consumption, agriculture and food security, industry and energy development. Assessing water availability, including the amount and type of precipitation, including snow and snow pack, groundwater and soil moisture, is critical to monitoring agricultural practices and water resource availability and providing interventions when necessary.

To read about the data or benefits and limitations of using remotely-sensed data, view the Agriculture and Water Resources Data Pathfinder page.

Precipitation

Precipitation

Near real-time IMERG Early Run Half-Hourly Image, acquired on November 12, 2019.

Near real-time IMERG Early Run Half-Hourly Image, acquired on November 12, 2019.

NASA’s Precipitation Measurement Missions (PMM) provide a continuous long-term record (over 20 years) of precipitation data through the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission. GPM, a follow-on mission for TRMM, provides even more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

The products from TRMM and GPM are available individually and have also been integrated with data from a global constellation of satellites to yield improved spatial/temporal precipitation estimates providing a temporal resolution of 30 minutes (in the case of GPM). The integrated products are the TRMM Multi-satellitE Precipitation Analysis (TMPA) and the Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG’s multiple runs accommodate different user requirements for latency and accuracy (Early = 4 hours, e.g., for flash flood events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research).

NASA, in collaboration with other agencies, has also developed models of precipitation, incorporating satellite information with ground-based data when available. These models are part of the Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates.

Science quality data products can be accessed via Earthdata Search:

  • TMPA from Earthdata Search
    Rainfall estimate at 3 hours, 1 day, or near real-time (NRT) and accumulated rainfall at 3 hours and 1 day. Data are in HDF format and can be opened using Panoply. Data are available from 1997.
  • IMERG from Earthdata Search
    Early, Late, and Final precipitation data on the half hour or 1-day timeframe. Data are in NetCDF or HDF format and can be opened using Panoply. Data are available from 2000.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

Near real-time data can be accessed from Worldview.

Daymet is a collection of gridded estimates of daily weather parameters. It is modeled on daily meteorological observations. Weather parameters in Daymet include daily minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii.

Daymet data can be retrieved in a variety of ways, including: Earthdata Search; an Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) API; ORNL DAAC tools; and through the Land Processes DAAC (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS).

Snow Cover/Snow Water Equivalent

Snow Cover/Snow Water Equivalent

Snow Water Equivalent over Tuolumne Basin June 4, 2017. Image credit: NASA Airborne Snow Observatory

Snow Water Equivalent over Tuolumne Basin June 4, 2017. Image credit: NASA Airborne Snow Observatory

Snow cover is the presence of snow on land or bodies of water; measurements are acquired during the daytime and under cloud-clear conditions. Snow Water Equivalent (SWE) is the amount of water contained in snowpack in the Northern and Southern Hemispheres measured in millimeters (mm). It is analogous to melting the snow and measuring the depth of the resulting pool of water. SWE measurements are useful for assessing both the potential surface runoff when the snow melts and the water availability for regions in lower elevations. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite measures snow cover while the Advanced Microwave Scanning Radiometer (AMSR) instrument measures snow water equivalent.

Science quality data products can be accessed via Earthdata Search; datasets are available as HDF5 files which can be opened using Panoply.

NRT data can be accessed via Worldview:

Daymet data can be retrieved in a variety of ways, including: Earthdata Search; an ORNL DAAC API; ORNL DAAC tools; and through the LP DAAC AρρEEARS.

Groundwater

Groundwater

Changes in groundwater storage can be measured from space using the Gravity Recovery And Climate Experiment (GRACE) data. Data are available from 2002 to the present; the data track anomalies (changes from the mean) and so are not representative of total water storage.

Terrestrial water storage anomalies from 2002-2014 for a California region. Overall there has been a decline general decline due to drought.

Terrestrial water storage anomalies from 2002-2014 for a California region. Overall there has been a decline general decline due to drought.

Note that there are several limitations with the GRACE data:

  • The resolution of the data are greater than 150,000km2 so it only measures change within large aquifers;
  • GRACE cannot detect issues of water quality (salt water intrusion, chemicals, etc.);
  • GRACE does not provide information on groundwater flow because the satellite only measures in one dimension, while groundwater flow is not limited to one dimension; and
  • GRACE does not provide information on whether the aquifer is confined or unconfined.

The value of GRACE data is evident when doing regional studies to determine general trends in groundwater storage.

Science quality data products can be accessed via Earthdata Search; datasets are available as NetCDF files which can be opened using Panoply or imported into a GIS system.

  • Groundwater Storage Percentile from Earthdata Search
    This serves as a drought indicator and the data are based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

GRACE observations are acquired through two formation-flying satellites. In processing the data, scientists must run a complex inversion algorithm with precise GPS information and acceleration corrections. Many parameter choices and solution strategies are possible. Three different teams have taken on this task—GeoForschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas (CSR), Austin, and NASA's Jet Propulsion Laboratory (JPL)—all producing slightly different results. Recent peer-reviewed papers found that the simple, arithmetic mean of JPL, CSR, and GFZ fields reduced the noise in the gravity field solutions. For more information on the three different solutions, see the JPL GRACE website’s Choosing a Solution. Data are represented as Water Equivalent Thickness (WET), which is a way of representing changes in the gravity field in hydrological units.

Water Equivalent Thickness (WET) from data run with each algorithm, that from the GeoForschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas, Austin (CSR) and the Jet Propulsion Laboratory (JPL). Th final is the arithmetic mean of the three, calculated in a GIS program.

Water Equivalent Thickness (WET) from GRACE data run with each algorithm, that from the GeoForschungsZentrum Potsdam (GFZ), the Center for Space Research at the University of Texas, Austin (CSR) and the Jet Propulsion Laboratory (JPL). Th final is the arithmetic mean of the three, calculated in a GIS program.

  • GRACE Groundwater WET: monthly data from 2002–2017 from each solution in ASCII or GeoTIFF. The NetCDF data are averaged 2002–2017 data. The mean of the three can be calculated in most GIS systems (see figure above).

Soil Moisture

Soil Moisture

Soil moisture is important for surface hydrology studies as it controls the amount of water that can infiltrate the ground, replenish aquifers, and contribute to excess runoff. As mentioned in the benefits and limitations section, current ground measurements of soil moisture are sparse and have limited coverage; satellite data help fill in those gaps.

Graph showing soil moisture compared to crop yield; indicates a positive correlation.Specifically for agriculture, observed variations in soil moisture, regardless of the data resolution, correlate very well with variations in crop yield. However, satellite data are limited by their relatively coarse resolution; the preferred measurement should be chosen based upon your needs. Utilizing a combination of both ground-based and remotely-sensed data provides for spatial and temporal data continuity.

NASA's Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top 5 cm of soil globally, every 2-3 days, at a resolution of 9-36 km. NASA, in collaboration with other agencies, has also developed models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of the LDAS, of which there is a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates of soil moisture and evapotranspiration.

Science quality data products can be accessed via Earthdata Search; datasets are available as HDF5 (SMAP) files which are also customizable to GeoTIFF.

The ORNL DAAC Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. See the Tools for Data Access and Visualization section for additional information.

AρρEEARS offers another option to simply and efficiently extract subsets, transform, and visualize SMAP data products.

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool, Giovanni. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

NRT imagery can be accessed via Worldview. NRT imagery is provided on a current, past week, and past month timeframe. For more information specific to droughts, see External Resources section.

Tools for Data Access and Visualization

Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | AρρEEARS | Soil Moisture Visualizer | MODIS/VIIRS Subsetting Tools Suite

Earthdata Search is a website for data discovery of Earth Observation data collections from NASA’s Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the Earth science disciplines. Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections.

Screenshot of the Search Earthdata site.


In the project area, you can customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML, or a GeoTIFF. You can also choose from a variety of projection options. Lastly you can subset the data, obtaining only the bands that are needed.

Earthdata Search customization tools diagram.

Panoply

HDF and NetCDF files can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.

HEG

The National Snow and Ice Data Center DAAC (NSIDC DAAC) has an HDF to GeoTIFF conversion tool (HEG), which allows you to geolocate, subset, stitch, and re-grid certain HDF-EOS datasets.

Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are the more popular ones.

  • Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  • Map animations are a means to observe spatial patterns and detect unusual events over time.
  • Area-averaged time series are used to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step.
  • Histogram plots are used to display the distribution of values of a data variable in a selected region and time interval.

For more detailed tutorials:

  • Giovanni How-To’s on the NASA GES DISC YouTube channel.
  • Data recipe for downloading a Giovanni map, as NetCDF, and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text.

Worldview

NASA’s Earth Observing System Data and Information System (EOSDIS) Worldview mapping application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.” This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA’s Global Imagery Browse Services (GIBS). Worldview now includes nine geostationary imagery layers from GOES-East, GOES-West and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the visualization of the differentiation between air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. The post-hurricane image on the left shows widespread outages around San Juan, including key hospital and transportation infrastructure.

AρρEEARS

AρρEEARS, from LP DAAC, offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AρρEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons.

Performing Area Extractions

After choosing to request an area extraction, you will be taken to the Extract Area Sample page where you will specify a series of parameters that are used to extract data for your area(s) of interest.

Spatial Subsetting

You can define your region of interest in three ways:

  • Upload a vector polygon file in shapefile format (you can upload a single file with multiple features or multipart single features). The .shp, .shx, .dbf, or .prj files must be zipped into a file folder to upload.
  • Upload a vector polygon file in GeoJSON format (can upload a single file with multiple features or multipart single features).
  • Draw a polygon on the map by clicking on the Bounding box or Polygon icons (single feature only).

Select the date range for your time period of interest.

Specify the range of dates for which you wish to extract data by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting dates a start and end date in the calendar.

Adding Data Layers

Enter the product short name (e.g., MOD09A1, WELDUSMO), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching your query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AρρEEARS.

Extracting an area in AppEEARS

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF-4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF-4 is selected, outputs will be grouped into .nc files by product and by feature.

If GeoTIFF is selected, you must select a projection

Interacting with Results

Once your request is completed, from the Explore Requests page, click the View icon in order to view and interact with your results. This will take you to the View Area Sample page.

The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AρρEEARS ID (aid). If your feature contains attribute table information, you can view the feature attribute table data by clicking on the Information icon to the right of the Feature dropdown. To view statistics from different features or layers, select a different aid from the Feature dropdown and/or a different layer of interest from the Layer dropdown.

Interpreting Results in AppEEARS

Be sure to check out the AppEEARS documentation to learn more about downloading the output GeoTIFF or netcdf-4 files.

Soil Moisture Visualizer

ORNL DAAC has developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of different soil moisture datasets over North America. The visualization tool incorporates in-situ, airborne, and remotely-sensed data into one easy to use platform. This integration helps to validate and calibrate the data, and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide.

To use the visualizer, select a dataset of interest under Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and relative time frames of data collection. Upon selection of the parameter, the tool displays a time series with available datasets. All measurements are volumetric soil moisture. Surface soil moisture is the daily average of measurements at 0-5 cm depth, and root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth. Lastly it provides data sources for download.

ORNL DAAC Soil Moisture Visualizer

The Soil Moisture Visualizer allows users to compare soil moisture measurements from multiple sources (figure legends, top left and bottom right) at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data from NASA’s Soil Moisture Active Passive (SMAP) Observatory are shown with data from in situ sensors across the 9-kilometer Equal-Area Scalable Earth (EASE) grid cell encompassing the Tonzi Ranch Fluxnet site in the Sierra Nevada foothills of California. Daily precipitation values for the site (purple spikes) are also provided for reference.

MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several MODIS and VIIRS Subset Tools for subsetting data.

  • With the Global Subset Tool, you can request a subset for any location on earth, provided as GeoTiff and in text format, including interactive time-series plots and more. Users specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, you can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/long. You will need an Earthdata account to request data.
  • With the Fixed Subsets Tool, you can download pre-processed subsets for 2000+ field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites.
  • With the Web Service, you can retrieve subset data (in real-time) for any location(s), time period and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into users' workflow.

Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

Water Management Resources

Water Management Resources

Water budgets for individual watersheds can be estimated using remotely-sensed data for precipitation, evapotranspiration, and runoff. All of the data can be obtained from the GLDAS at the same temporal and spatial resolution through Giovanni. A few things to consider: note the units—calculations may have to be done in a GIS system to change to the units needed. For example, precipitation and ET are in kg m2/s; for annual data, you would need to multiply the data by 3600 s/hr, by 24 hr/day, and then by 365 days/year. Runoff data are in the same units above but are collected at 3-hour intervals and so need to be multiplied by 8 (3 hr/day) and then by 365 days/year. Once the data are in the appropriate units, you can use the raster calculation tool to subtract ET and runoff from precipitation to get an estimated water budget. Numerous statistical analyses available within a GIS program can provide additional information on trends.

Last Updated: Dec 4, 2019 at 3:24 PM EST