The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format.

Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.This data product, GLDAS-2.0 1.0 degree monthly, was reprocessed and replaced its previous data product on November 19, 2019. The data product was generated through temporal averaging of the reprocessed 3-hourly data, contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.The GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The land mask was modified to accommodate the river routing scheme included in the simulations in the fall 2019 update. In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.

GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive.

This data product is for GLDAS-2.1 Catchment 3-hourly 1.0 degree data from the main production stream. It was simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format.

The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.

In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.

If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.This data set, GLDAS-2.0 monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.The GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.This data set, GLDAS-2.0 VIC monthly 1.0 degree, contains a series of land surface variables generated through temporal averaging of GLDAS-2.0 3-hourly data simulated with the VIC 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in NetCDF format.The GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.2 is new to the GES DISC archive and currently includes a main product from CLSM-F2.5 with Data Assimilation for the Gravity Recovery and Climate Experiment (GRACE-DA) from February 2003 to present. The GLDAS-2.2 data are available in two production streams: one with GRACE data assimilation outputs (the main production stream), and one without GRACE-DA (the early production stream). Since the GRACE data have a 2-6 month latency, the GLDAS-2.2 data are first created without GRACE-DA, and are designated as the Early Product (EP), with about 1 month latency. Once the GRACE data become available, the GLDAS-2.2 data are processed with GRACE-DA in the main production stream and are removed from the Early Product archive. This data product is an Early Product for GLDAS-2.2 Catchment Land Surface Model daily 0.25 x 0.25 degree with GRACE-DA. The GLDAS-2.2 GARCE-DA product was simulated with Catchment-F2.5 in Land Information System (LIS) Version 7. The data product contains 24 land surface fields from February 1, 2003 to present.The simulation started on February 1, 2003 using the conditions from the GLDAS-2.0 Daily Catchment model simulation, forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System. The total terrestrial water anomaly observation from GRACE satellite was assimilated (Li et al, 2019). Due to the data agreement with ECMWF, this GLDAS-2.2 daily product does not include the meteorological forcing fields.The GLDAS-2.2 data are archived and distributed in NetCDF format.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. 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-2.1). 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!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. 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.

Melting snowpack is a key part of the spring water budget in many parts of the world. Like a natural reservoir, snowpack stores winter precipitation and releases it as runoff over the course of many months. Where summer rains are scarce snowpack provides crucial base flow without which rivers might go dry. Where summer rains are torrential, this exacerbates the flooding and can lead to the loss of lives. This map contains a historical record showing the water stored in snowpack during each month from March 2000 to the present. It is not a map of snow depth, but of snow water equivalent, which is the amount of water that would be produced if all the snow melted. For fresh snow, this can be anywhere from 5% to 20% the depth of the snow, depending on temperature (snow tends to be fluffier at lower temperatures). As the snow settles and melts, it becomes more dense, up to 40% or 50% in the spring. Temperature, albedo (the reflective property of the snow), density, and volume all affect the melting rate of the snowpack. Additionally, melting rate is influenced by wind, relative humidity, air temperature and solar radiation.Dataset SummaryThe GLDAS Snowpack layer is a time-enabled image service that shows average monthly snowpack from 2000 to present, measured in millimeters of snow water equivalent. 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-2.1). 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!Phenomenon Mapped: SnowpackUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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 Desktop. Is useful for scientific modeling, but only at global scales. The GLDAS snowpack data is useful for modeling, but only at global scales. By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean, instead of total snowpack. Mean snowpack for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. 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.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.This data set, GLDAS-2.0 1.0 degree 3-hourly, contains a series of land surface variables simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format. The GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. This data product is for GLDAS-2.1 Catchment monthly 1.0 degree data from the main production stream. It was generated through temporal averaging of GLDAS-2.1 3-hourly data simulated with the Catchment-F2.5 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.This data product, GLDAS-2.0 0.25 degree monthly, was reprocessed and replaced its previous data product on November 19, 2019. The data product was generated through temporal averaging of the reprocessed 3-hourly data, contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.The GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The land mask was modified to accommodate the river routing scheme included in the simulations in the fall 2019 update. In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

The goal of NLDAS is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. NLDAS is currently running in near real-time on a 1/8th-degree grid over central North America; retrospective NLDAS datasets and simulations also extend back to January 1979. NLDAS constructs a forcing dataset from gauge-based observed precipitation data (temporally disaggregated using Stage II radar data), bias-correcting shortwave radiation, and surface meteorology reanalyses to drive several different LSMs to produce model outputs of surface fluxes, soil moisture, and snow cover. NLDAS is a collaboration project among several groups: NOAA/NCEP's Environmental Modeling Center (EMC), NASA's Goddard Space Flight Center (GSFC), Princeton University, the University of Washington, the NOAA/NWS Office of Hydrological Development (OHD), and the NOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project with support from NOAA's Climate Prediction Program for the Americas (CPPA). Data from the project can be accessed from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) as well as from the NCEP/EMC NLDAS website. This service provides access to NASA's North American Land Data Assimilation System (NLDAS) hourly Mosaic land surface model data.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.This data product, GLDAS-2.0 0.25 degree 3-hourly, was reprocessed and replaced its previous data product on November 27, 2019. The data product contains a series of land surface parameters simulated from the Noah Model 3.6, and currently covers from January 1948 to December 2014, but will be extended as the data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.The GLDAS-2.0 model simulations were initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Noah model uses the Modified IGBP MODIS 20-category vegetation classification and the soil texture based on the Hybrid STATSGO/FAO) datasets. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. 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-2.1). 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!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. 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.

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present, measured as the millimeters of water contained within four different depth levels. 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-2.1). 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!Phenomenon Mapped: Soil MoistureUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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 Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean soil moisture for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. If you do this, make sure to also select a specific variable. The minimum time extent is one month, and the maximum is 8 years. Variables: This layer has five variables, corresponding to different depth levels. By default total is shown, but you can view an individual depth level using the multidimensional filter, or by applying the relevant raster function. 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.

Most of us understand the hydrologic cycle in terms of the visible paths that water can take such as rainstorms, rivers, waterfalls and lakes. However, an even larger volume of water flows through the air all around us in two invisible paths: evaporation and transpiration. These two paths together are referred to as evapotranpsiration (ET), and claim 61% of all terrestrial precipitation. Solar radiation, air temperature, wind speed, soil moisture, and land cover all affect the rate of evapotranspiration, which is a major driver of the global water cycle, and key component of most catchments' water budget. This map contains a historical record showing the volume of water lost to evapotranspiration globally during each month from March 2000 to the present.Dataset SummaryThe GLDAS Evapotranspiration layer is a time-enabled image service that shows total actual evapotranspiration monthly from 2000 to the present, measured in millimeters of water loss. 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-2.1). 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!Phenomenon Mapped: EvapotranspirationUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat 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. By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean evapotranspiration for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. 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.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. This data product is for GLDAS-2.1 VIC monthly 1.0 degree data from the main production stream. It was generated through temporal averaging of GLDAS-2.1 3-hourly data simulated with the VIC 4.1.2 Land Surface Model in Land Information System (LIS) Version 7. The data product contains 34 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

The Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids, Version 1 data set identifies and characterizes surpluses and deficits of freshwater, and the parameters determining these anomalies, at monthly intervals over the period January 1948 to December 2014. The data set uses the land surface model outputs from NASA's Global Land Data Assimilation System, covering the global extent, to generate anomaly values for the following parameters at a gridded resolution of 0.25 degrees: temperature, precipitation, soil moisture, potential minus actual evapotranspiration, runoff, total blue water (flow-accumulated runoff), composite index of water surplus, and composite index of water deficits. These data are provided in terms of return periods, scientific Units, and standardized (normalized) anomalies, and are computed over 1-month, 3-month, 6-month, and 12-month temporal periods of accumulation, referred to as integration periods. Anomaly values are present in terms of return periods with respect to a fitted Generalized Extreme Value (GEV) probability distribution function over a historical baseline period of January 1950 to December 2009, at a global spatial resolution of 0.25 degrees over the monthly, 3-month, 6-month, and 12-month periods of integration. Parameter values (_location, scale, shape) of the fitted GEV probability distribution, which are fit separately for each calendar month, are distributed per parameter for each integration period.

The land water content contained in this dataset were generated from The Global Land Data Assimilation System (GLDAS). GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on GLDAS please visit http://grace.jpl.nasa.gov/data/get-data/land-water-content/ . The data are available on monthly 1 degree grids in NetCDF format.
This total water content is directly comparable to what the Gravity Recovery and Climate Experiment (GRACE) measures over land. The Greenland values should NOT be used, the model is not valid there. The twin satellites GRACE, launched in March of 2002, are making detailed monthly measurements of Earth's gravity field changes. These observations can detect regional mass changes of Earth's water reservoirs over land, ice and oceans. GRACE measures gravity variations by relating it to the distance variations between the two satellites, which fly in the same orbit, separated by about 240 km at an altitude of ~450 km.