Analysis of Drought Trends and Severity Using Standard Anomalies: Case of Baringo County, Kenya

Abstract

Increased frequency, severity and duration of drought events in arid and semi-arid lands (ASALs) of Kenya increase scarcity in water and pastures that support livestock assets. This destabilizes the livelihood base dependent on livestock assets. Drought analysis can provide early warning of the drought events and inform actions to reduce vulnerability of pastoral households to drought effects. Drought studies use different methods to analyse drought events. The most commonly used methods include: Percent of normal, Deciles, Palmer drought severity index (PDSI), Surface water supply index (SWSI) and Normalised difference vegetation index (NDVI). The objective of this study was to use the estimation of standardized anomalies (SA (t) = {SP (t) - μ} ÷ {σ}) to characterise trends and severity of droughts in Baringo County. Rainfall data for the period 1970 – 2013 for two rainfall stations (Nginyang and Perkerra) in the study area was collected from Kenya Meteorological Department. Through literature review, the present study confirmed that the standard anomalies method requires only rainfall data that is the most accessible meteorological data in most countries unlike other methods such as PDSI that is based on the supply and demand concept on water balance equation, SWSI that use monthly data for precipitation, reservoirs, snowpack and stream flow and NDVI that monitor rangeland conditions, desertification and changes in the land use systems. The standard anomalies method ensures that the spatial and temporal frequency of extreme events is consistent and therefore useful in establishing inter and intra-annual and seasonal drought variability across the study area. Through the use of estimation of standard anomalies, the study established that the study area experienced extreme drought events of SA(t) < -0.9 during the years 1972/1973. 1976, 1980, 1984, 1986, 1995/1996, 2001-2004, 2006 and 2008. These results concur with observed drought events and perceived drought/rainfall events over the study period, an indication that the method yields accurate results. The study concludes that estimation of standard anomalies is an efficient method of analysing drought events. Through time-series plots of the standard anomalies, the method deduced that the study area will generally continue to experience drought events. The study recommends use of estimation of standard anomalies in analysing drought events and a tool in decision making regarding adoption of appropriate strategies to respond to drought events such as diversification, livestock off-sets, pastoral migration among others.