Predictability of the Normalized Difference Vegetation Index in Kenya and Potential Applications as an Indicator of Rift Valley Fever Outbreaks in the Greater Horn of Africa

• Published in: Journal of climate 2006-05, Vol.19 (9), p.1673-1687
• Main Authors: Indeje, Matayo, Ward, M. Neil, Ogallo, Laban J., Davies, Glyn, Dilley, Maxx, Anyamba, Assaf
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Climate; Climate models; Climatic zones; Earth, ocean, space; Economic importance; Environmental conditions; Environmental monitoring; Epidemics; Exact sciences and technology; External geophysics; Famine; Fever; Forecasting models; Fundamental and applied biological sciences. Psychology; Global climate; Livestock; Medically important nuisances and vectors, pests of stored products and materials: population survey and control; Meteorology; Oceans; Precipitation; Predictability; Rain; Seasons; Time series; Time series forecasting; Vector-borne diseases; Vectors. Intermediate hosts; Vegetation; Weather analysis and prediction; Weather forecasting
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI3708.1

In this paper the progress made in producing predictions of the Normalized Difference Vegetation Index (NDVI) over Kenya in the Greater Horn of Africa (GHA) for the October–December (OND) season is discussed. Several studies have identified a statistically significant relationship between rainfall and NDVI in the region. Predictability of seasonal rainfall by global climate models (GCMs) during the OND season over the GHA has also been established as being among the best in the world. Information was extracted from GCM seasonal prediction output using statistical transformations. The extracted information was then used in the prediction of NDVI. NDVI is akey variable for management of various climate-sensitive problems. For example, it has been shown to have the potential to predict environmental conditions associated with Rift Valley Fever (RVF) viral activity and this isreferred to throughout the paper as a motivation for the study. RVF affects humans andlivestock and is particularly economically important in the GHA. The establishment of predictability for NDVI in this paper is thereforepart of a methodology that could ultimately generate information useful for managing RVF in livestock in the GHA. It has been shown that NDVI can be predicted skillfully over the GHA with a 2–3-month lead time. Such information is crucial for tailoring forecast information to support RVF monitoring and prediction over the region, as well as many other potential applications (e.g., livestock forage estimation). More generally, the Famine Early Warning System (FEWS), a project of the U.S. Agency for International Development (USAID) and the National Aeronautics and Space Administration (NASA) and other specialized technical centers routinely use NDVI images tomonitor environmental conditions worldwide. The high predictability for NDVI established in this paper could therefore supplement the routine monitoring of environmental conditions for a wide range of applications.