Set data FLDAS (McNally et al. 2017) dirancang untuk membantu penilaian ketahanan pangan di negara berkembang dengan data yang terbatas. Data ini mencakup informasi tentang banyak variabel terkait iklim, termasuk kandungan kelembapan, kelembapan, evapotranspirasi, suhu tanah rata-rata, total laju presipitasi, dll.
Ada beberapa set data FLDAS yang berbeda; set data ini menggunakan model permukaan Noah versi 3.6.1 dengan curah hujan per jam CHIRPS-6 yang telah di-downscale menggunakan NASA Land Surface Data Toolkit, yang merupakan bagian dari framework Land Information System. Disagregasi temporal diperlukan agar input curah hujan harian dapat digunakan dalam penghitungan keseimbangan energi dan air
Untuk data paksa, simulasi ini menggunakan kombinasi data versi baru analisis Retrospektif Era Modern untuk Riset dan Aplikasi versi 2 (MERRA-2) dan data Curah Hujan Infra Merah dengan data Stasiun dari Climate Hazards Group (CHIRPS), yaitu set data curah hujan kuasi-global yang dirancang untuk pemantauan kekeringan musiman dan analisis tren (Funk et al., 2015).
Distribusi data dari Goddard Earth Sciences Data and Information Services Center (GES DISC) didanai oleh Science Mission Directorate (SMD) NASA. Sesuai dengan Kebijakan Data dan Informasi Ilmu Bumi NASA, data dari arsip GES DISC tersedia gratis untuk komunitas pengguna.
Untuk mengetahui informasi selengkapnya, buka halaman Kebijakan Data GES DISC.
Kutipan
Kutipan:
Jika Anda menggunakan data ini dalam riset atau aplikasi, sertakan referensi dalam publikasi Anda yang serupa dengan contoh berikut:
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), Diakses:
[Tanggal Akses Data], 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). Sistem asimilasi data lahan untuk aplikasi keamanan pangan dan air di Afrika sub-Sahara. Scientific Data, 4, 170012.
Kumpulan data FLDAS (McNally et al. 2017) dirancang untuk membantu penilaian ketahanan pangan di negara berkembang dengan data yang jarang. Data ini mencakup informasi tentang banyak variabel terkait iklim, termasuk kandungan kelembapan, kelembapan, evapotranspirasi, suhu tanah rata-rata, tingkat presipitasi total, dll. Ada beberapa set data FLDAS yang berbeda; yang ini menggunakan versi Noah …
[null,null,[],[[["\u003cp\u003eThe FLDAS dataset provides monthly climate-related variables like moisture, humidity, evapotranspiration, soil temperature, and precipitation for food security assessments, especially in developing countries.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset, spanning from 1982 to 2024, utilizes the Noah 3.6.1 surface model with CHIRPS-6 hourly rainfall downscaled using the NASA Land Surface Data Toolkit.\u003c/p\u003e\n"],["\u003cp\u003eIt incorporates MERRA-2 and CHIRPS data for forcing and offers a global coverage at a resolution of 11132 meters.\u003c/p\u003e\n"],["\u003cp\u003eFreely available through NASA's GES DISC, users are encouraged to cite the dataset and related publications when using it for research or applications.\u003c/p\u003e\n"]]],["The FLDAS dataset, available from 1982-01-01 to 2025-01-01, is provided monthly by NASA GES DISC. It uses the Noah 3.6.1 surface model and combines MERRA-2 and CHIRPS data. Designed for food security assessments, the dataset includes climate variables like evapotranspiration, humidity, soil temperature, precipitation, and soil moisture across various depths, plus wind. It is available on Google Earth Engine, which is free for research, education and nonprofit use, where you can access the dataset using the provided code snippet.\n"],null,["# FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System\n\nDataset Availability\n: 1982-01-01T00:00:00Z--2025-07-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [NASA GES DISC at NASA Goddard Space Flight Center](https://doi.org/10.5067/5NHC22T9375G)\n\nCadence\n: 1 Month\n\nTags\n:\n [climate](/earth-engine/datasets/tags/climate) [cryosphere](/earth-engine/datasets/tags/cryosphere) [evapotranspiration](/earth-engine/datasets/tags/evapotranspiration) [humidity](/earth-engine/datasets/tags/humidity) [ldas](/earth-engine/datasets/tags/ldas) [monthly](/earth-engine/datasets/tags/monthly) [nasa](/earth-engine/datasets/tags/nasa) [precipitation](/earth-engine/datasets/tags/precipitation) [runoff](/earth-engine/datasets/tags/runoff) [snow](/earth-engine/datasets/tags/snow) [soil](/earth-engine/datasets/tags/soil) [soil-moisture](/earth-engine/datasets/tags/soil-moisture) [soil-temperature](/earth-engine/datasets/tags/soil-temperature) [temperature](/earth-engine/datasets/tags/temperature) [water-vapor](/earth-engine/datasets/tags/water-vapor) [wind](/earth-engine/datasets/tags/wind) \n famine \nfldas \n\n#### Description\n\nThe FLDAS dataset (McNally et al. 2017), was designed to assist with food\nsecurity assessments in data-sparse, developing country settings. It\nincludes information on many climate-related variables including moisture\ncontent, humidity, evapotranspiration, average soil temperature, total\nprecipitation rate, etc.\n\nThere are multiple different FLDAS datasets; this one uses Noah version\n3.6.1 surface model with CHIRPS-6 hourly rainfall that has been\ndownscaled using the [NASA Land Surface Data Toolkit](https://lis.gsfc.nasa.gov/software/ldt).\nwhich is part of the [Land Information System\nframework](/earth-engine/datasets/catalog/LIS;%20%5Bhttps:/lis.gsfc.nasa.gov/%5D(https:/lis.gsfc.nasa.gov/)). Temporal desegregation is required so that daily\nrainfall inputs can be used in both energy and water balance calculations\n\nFor forcing data, this simulation uses a combination of the new version of Modern-Era\nRetrospective analysis for Research and Applications version 2 (MERRA-2) data and Climate\nHazards Group InfraRed Precipitation with Station data (CHIRPS), a quasi-global rainfall\ndataset designed for seasonal drought monitoring and trend analysis (Funk et al., 2015).\n\nDocumentation:\n\n- [Readme](https://hydro1.gesdisc.eosdis.nasa.gov/data/FLDAS/FLDAS_NOAH01_C_GL_M.001/doc/README_FLDAS.pdf)\n\n- [How-to](https://disc.gsfc.nasa.gov/information/howto?tags=hydrology)\n\n- [GES DISC Hydrology Documentation](https://disc.gsfc.nasa.gov/information/documents?title=Hydrology%20Documentation)\n\n### Bands\n\n\n**Pixel Size**\n\n11132 meters\n\n**Bands**\n\n| Name | Units | Pixel Size | Description |\n|--------------------------|-----------------|------------|---------------------------------------------|\n| `Evap_tavg` | kg/m\\^2/s | meters | Evapotranspiration |\n| `LWdown_f_tavg` | W/m\\^2 | meters | Downward longwave radiation flux |\n| `Lwnet_tavg` | W/m\\^2 | meters | Net longwave radiation flux |\n| `Psurf_f_tavg` | Pa | meters | Surface pressure |\n| `Qair_f_tavg` | Mass fraction | meters | Specific humidity |\n| `Qg_tavg` | W/m\\^2 | meters | Soil heat flux |\n| `Qh_tavg` | W/m\\^2 | meters | Sensible heat net flux |\n| `Qle_tavg` | W/m\\^2 | meters | Latent heat net flux |\n| `Qs_tavg` | kg/m\\^2/s | meters | Storm surface runoff |\n| `Qsb_tavg` | kg/m\\^2/s | meters | Baseflow-groundwater runoff |\n| `RadT_tavg` | K | meters | Surface radiative temperature |\n| `Rainf_f_tavg` | kg/m\\^2/s | meters | Total precipitation rate |\n| `SnowCover_inst` | | meters | Snow cover fraction |\n| `SnowDepth_inst` | m | meters | Snow depth |\n| `Snowf_tavg` | kg/m\\^2/s | meters | Snowfall rate |\n| `SoilMoi00_10cm_tavg` | Volume fraction | meters | Soil moisture (0 - 10 cm underground) |\n| `SoilMoi10_40cm_tavg` | Volume fraction | meters | Soil moisture (10 - 40 cm underground) |\n| `SoilMoi100_200cm_tavg` | Volume fraction | meters | Soil moisture (100 - 200 cm underground) |\n| `SoilMoi40_100cm_tavg` | Volume fraction | meters | Soil moisture (40 - 100 cm underground) |\n| `SoilTemp00_10cm_tavg` | K | meters | Soil temperature (0 - 10 cm underground) |\n| `SoilTemp10_40cm_tavg` | K | meters | Soil temperature (10 - 40 cm underground) |\n| `SoilTemp100_200cm_tavg` | K | meters | Soil temperature (100 - 200 cm underground) |\n| `SoilTemp40_100cm_tavg` | K | meters | Soil temperature (40 - 100 cm underground) |\n| `SWdown_f_tavg` | W/m\\^2 | meters | Surface downward shortwave radiation |\n| `SWE_inst` | kg/m\\^2 | meters | Snow water equivalent |\n| `Swnet_tavg` | W/m\\^2 | meters | Net shortwave radiation flux |\n| `Tair_f_tavg` | K | meters | Near surface air temperature |\n| `Wind_f_tavg` | m/s | meters | Near surface wind speed |\n\n### Terms of Use\n\n**Terms of Use**\n\nDistribution of data from the Goddard Earth Sciences\nData and Information Services Center (GES DISC) is funded by NASA's\nScience Mission Directorate (SMD). Consistent with NASA [Earth\nScience Data and Information Policy](https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy/),\ndata from the GES DISC archive are available free to the user community.\nFor more information visit the GES DISC [Data Policy](https://disc.sci.gsfc.nasa.gov/citing)\npage.\n\n### Citations\n\nCitations:\n\n- If you use these data in your research or applications, please include a\n reference in your publication(s) similar to the following example:\n Amy McNally NASA/GSFC/HSL (2018), FLDAS Noah Land Surface Model L4 Global\n Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, MD, USA, Goddard\n Earth Sciences Data and Information Services Center (GES DISC), Accessed:\n \\[Data Access Date\\], [doi:10.5067/5NHC22T9375G](https://doi.org/10.5067/5NHC22T9375G)\n- McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C.,\n Peters-Lidard, C.D., \\& Verdin, J. P. (2017). A land data assimilation system for\n sub-Saharan Africa food and water security applications. Scientific Data, 4, 170012.\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset=ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001')\n .filter(ee.Filter.date('2018-11-01', '2018-12-01'));\nvar layer = dataset.select('Evap_tavg');\n\nvar band_viz = {\n min: 0.0,\n max: 0.00005,\n opacity: 1.0,\n palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']\n};\n\nMap.setCenter(30.0, 30.0, 2);\nMap.addLayer(layer, band_viz, 'Average Evapotranspiration');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001) \n[FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System](/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001) \nThe 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 ... \nNASA/FLDAS/NOAH01/C/GL/M/V001, climate,cryosphere,evapotranspiration,humidity,ldas,monthly,nasa,precipitation,runoff,snow,soil,soil-moisture,soil-temperature,temperature,water-vapor,wind \n1982-01-01T00:00:00Z/2025-07-01T00:00:00Z \n-60 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://doi.org/10.5067/5NHC22T9375G)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001)"]]
