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 accessAbstract
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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Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.66 · Cited by 6,362
Self-citationUpdated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset
Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014) · International Journal of Climatology · doi:10.1002/joc.3711 · Cited by 6,094
The Global Land Data Assimilation System
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., et al. (2004) · Bulletin of the American Meteorological Society · doi:10.1175/bams-85-3-381 · Cited by 5,614
Self-citationXie, P., & Arkin, P. A. (1997) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(1997)078<2539:gpayma>2.0.co;2 · Cited by 4,307
Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994) · Journal of Geophysical Research: Atmospheres · doi:10.1029/94jd00483 · Cited by 4,051
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.08.016 · Cited by 3,473
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., & Tarpley, J. D. (2003) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2002jd003296 · Cited by 3,163
Global land cover classification at 1 km spatial resolution using a classification tree approach
Hansen, M. C., Defries, R. S., Townshend, J. R. G., & Sohlberg, R. (2000) · International Journal of Remote Sensing · doi:10.1080/014311600210209 · Cited by 2,280
Sheffield, J., Goteti, G., & Wood, E. F. (2006) · Journal of Climate · doi:10.1175/jcli3790.1 · Cited by 1,942
Cosby, B. J., Hornberger, G. M., Clapp, R. B., & Ginn, T. R. (1984) · Water Resources Research · doi:10.1029/wr020i006p00682 · Cited by 1,655
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Hermans, K., & McLeman, R. (2021) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2021.04.013 · Cited by 242
Attribution analysis of the Ethiopian drought of 2015
Philip, S., Kew, S. F., Jan van Oldenborgh, G., Otto, F., O’Keefe, S., Haustein, K., King, A., Zegeye, A., Eshetu, Z., Hailemariam, K., et al. (2018) · Journal of Climate · doi:10.1175/jcli-d-17-0274.1 · Cited by 170
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Frau, D., Moran, B. J., Arengo, F., Marconi, P., Battauz, Y., Mora, C., Manzo, R., Mayora, G., & Boutt, D. F. (2021) · Hydrology · doi:10.3390/hydrology8040164 · Cited by 25
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
- Published
- 2017-02-14
- Volume
- 4
- Issue
- 1
- Pages
- 170012-170012
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2588865615
- Cited by
- 632
- References
- 52
- Type
- data paper
- Language
- en
- Open access
- gold
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