Predictability of NDVI in semi-arid African regions

Abstract

In semi-arid Africa, rainfall variability is an important issue for ecosystems and agricultural activities. However, due to its discrete nature in time and space, rainfall is difficult to measure, quantify, and predict. In the dry tropics, a good proxy for rainfall is vegetation activity since this parameter is well correlated with rainfall variations. In this study, over 20 years of Normalized Difference Vegetative Index (NDVI) data from the Advanced Very High Resolution Radiometers are used. The goal is to assess the skill of linear statistical models in estimating regional NDVI interannual variability based on ocean and atmospheric fields (but not rainfall) and then to hindcast it with a 1- to 2-month lead-time. Three semi-arid areas of ∼150 000 km2 located in Western, Southern, and Eastern Tropical Africa are considered for this purpose. The predictors are: the Nino3.4 sea surface temperature index, the main modes of National Center for Environmental Prediction (NCEP) surface temperature variability in a window centered over Africa, and regional-scale indices based on NCEP surface temperatures and atmospheric variables (relative humidity, geopotential heights, and winds). The regional indices, which are physically and statistically robust, are generally asynchronous with the NDVI predictand. The statistical models, based on linear multiple regressions, give significant results, and the correlation between observed and cross-validated NDVI is 0.67 in Southern Africa, 0.76 for the long rains and 0.83 for the short rains in Eastern Africa, and 0.88 in Western Africa. The results have implications for (1) better understanding the role of El Nino/Southern Oscillation in semi-arid Africa, and (2) highlight the importance of regional climate processes for vegetation growth at these scales, notably the role played by the Mediterranean Sea and its influence on the West African monsoon. The predictability of NDVI over these African regions is discussed.