Monitoring drought in Ghana using TAMSAT‐ALERT: a new decision support system

Abstract

Approximately 886 million people in Africa rely on agriculture as their main means of survival. They are therefore susceptible to changes in seasonal rains from year to year that can result in agricultural drought. Agricultural drought is determined by low soil moisture content. Soil moisture responds to rainfall, but also depends on many other factors, including the soil characteristics and, crucially, on the past soil moisture. Here we demonstrate that predictive skill can be gained from knowledge of the current state of the land surface – how wet or dry the soil is – as the growing season evolves. This skill arises from the land surface memory – the soil moisture content at a particular time depends to a large extent on the historical soil moisture. By forcing a land surface model with observed data up to a ‘present day’ and then forward in time with climatological data (to represent the range of possible future conditions) we show that it is possible to be confident of an ensuing agricultural drought several weeks before the end of the growing season. This system is illustrated using results from an operational trial for Tamale in northern Ghana.