FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System

NASA/FLDAS/NOAH01/C/GL/M/V001
Dataset Availability
1982-01-01T00:00:00 - Present
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("NASA/FLDAS/NOAH01/C/GL/M/V001")
Tags
famine evapotranspiration humidity runoff snow temperature wind soil-temperature soil-moisture climate nasa ldas fldas monthly

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).

Bands

Resolution
0.1 arc degrees

Bands

Name Units Description
Evap_tavg kg m-2 s-1

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 kg kg-1

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-1

Storm surface runoff

Qsb_tavg kg m-2 s-1

Baseflow-groundwater runoff

RadT_tavg K

Surface radiative temperature

Rainf_f_tavg kg m-2 s-1

Total precipitation rate

SnowCover_inst

Snow cover fraction

SnowDepth_inst m

Snow depth

Snowf_tavg kg m-2 s-1

Snowfall rate

SoilMoi00_10cm_tavg m^3 m-3

Soil moisture (0 - 10 cm underground)

SoilMoi10_40cm_tavg m^3 m-3

Soil moisture (10 - 40 cm underground)

SoilMoi100_200cm_tavg m^3 m-3

Soil moisture (100 - 200 cm underground)

SoilMoi40_100cm_tavg m^3 m-3

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-1

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

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], 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 in Earth Engine

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');


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