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Cited by 632

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

A land data assimilation system for sub-Saharan Africa food and water security applications

Open access
McNally, Amy, Arsenault, Kristi, Kumar, Sujay, Shukla, Shraddhanand, Peterson, Pete, Wang, Shugong, Funk, Chris, Peters-Lidard, Christa D., Verdin, James P.·2017·Scientific Data
DOI 10.1038/sdata.2017.12
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Abstract

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

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Metadata

Authors
McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., Funk, C., Peters-Lidard, C. D., & Verdin, J. P.
Venue
Scientific Data
Publisher
Springer Science and Business Media LLC
Published
2017-02-14
Volume
4
Issue
1
Pages
170012-170012
ISSN
2052-4463 (ISSN-L)
DOI
10.1038/sdata.2017.12
OpenAlex
W2588865615
Cited by
632
References
52
Type
data paper
Language
en
Open access
gold

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