- Dataset Availability
- 1982-01-01T00:00:00Z–2024-10-01T00:00:00Z
- Dataset Provider
- NASA GES DISC at NASA Goddard Space Flight Center
- Earth Engine Snippet
-
ee.ImageCollection("NASA/FLDAS/NOAH01/C/GL/M/V001")
- Cadence
- 1 Month
- Tags
Description
The FLDAS dataset (McNally et al. 2017), was designed to assist with food security assessments in data-sparse, developing country settings. It includes information on many climate-related variables including moisture content, humidity, evapotranspiration, average soil temperature, total precipitation rate, etc.
There are multiple different FLDAS datasets; this one uses Noah version 3.6.1 surface model with CHIRPS-6 hourly rainfall that has been downscaled using the NASA Land Surface Data Toolkit. which is part of the Land Information System framework. Temporal desegregation is required so that daily rainfall inputs can be used in both energy and water balance calculations
For forcing data, this simulation uses a combination of the new version of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), a quasi-global rainfall dataset designed for seasonal drought monitoring and trend analysis (Funk et al., 2015).
Documentation:
Bands
Resolution
11132 meters
Bands
| Name | Units | Description |
|---|---|---|
Evap_tavg |
kg/m^2/s | Evapotranspiration |
LWdown_f_tavg |
W/m^2 | Downward longwave radiation flux |
Lwnet_tavg |
W/m^2 | Net longwave radiation flux |
Psurf_f_tavg |
Pa | Surface pressure |
Qair_f_tavg |
Mass fraction | Specific humidity |
Qg_tavg |
W/m^2 | Soil heat flux |
Qh_tavg |
W/m^2 | Sensible heat net flux |
Qle_tavg |
W/m^2 | Latent heat net flux |
Qs_tavg |
kg/m^2/s | Storm surface runoff |
Qsb_tavg |
kg/m^2/s | Baseflow-groundwater runoff |
RadT_tavg |
K | Surface radiative temperature |
Rainf_f_tavg |
kg/m^2/s | Total precipitation rate |
SnowCover_inst |
Snow cover fraction |
|
SnowDepth_inst |
m | Snow depth |
Snowf_tavg |
kg/m^2/s | Snowfall rate |
SoilMoi00_10cm_tavg |
Volume fraction | Soil moisture (0 - 10 cm underground) |
SoilMoi10_40cm_tavg |
Volume fraction | Soil moisture (10 - 40 cm underground) |
SoilMoi100_200cm_tavg |
Volume fraction | Soil moisture (100 - 200 cm underground) |
SoilMoi40_100cm_tavg |
Volume fraction | Soil moisture (40 - 100 cm underground) |
SoilTemp00_10cm_tavg |
K | Soil temperature (0 - 10 cm underground) |
SoilTemp10_40cm_tavg |
K | Soil temperature (10 - 40 cm underground) |
SoilTemp100_200cm_tavg |
K | Soil temperature (100 - 200 cm underground) |
SoilTemp40_100cm_tavg |
K | Soil temperature (40 - 100 cm underground) |
SWdown_f_tavg |
W/m^2 | Surface downward shortwave radiation |
SWE_inst |
kg/m^2 | Snow water equivalent |
Swnet_tavg |
W/m^2 | Net shortwave radiation flux |
Tair_f_tavg |
K | Near surface air temperature |
Wind_f_tavg |
m/s | Near surface wind speed |
Terms of Use
Terms of Use
Distribution of data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) is funded by NASA's Science Mission Directorate (SMD). Consistent with NASA Earth Science Data and Information Policy, data from the GES DISC archive are available free to the user community. For more information visit the GES DISC Data Policy page.
Citations
If you use these data in your research or applications, please include a reference in your publication(s) similar to the following example: Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], doi:10.5067/5NHC22T9375G
McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C.D., & Verdin, J. P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4, 170012.
Explore with Earth Engine
Code Editor (JavaScript)
var dataset=ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001') .filter(ee.Filter.date('2018-11-01', '2018-12-01')); var layer = dataset.select('Evap_tavg'); var band_viz = { min: 0.0, max: 0.00005, opacity: 1.0, palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red'] }; Map.setCenter(30.0, 30.0, 2); Map.addLayer(layer, band_viz, 'Average Evapotranspiration');