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.
Highlights
The distribution of values obtained represents the range within which the growing-season mean soil moisture is likely to lie by the end of the season
This distribution is used to determine the probability that the mean soil moisture will be below an arbitrary drought threshold
Monitoring of drought risk is of key importance for regions of Africa that depend on rainfed agriculture
Summary
A land surface model translates from the difference between the moisture meteorological driving data, such as rain- extracted by vegetation and the fluxes in. There is a clear need for usable risk assessment and decision support tools for the management of agricultural drought (soil moisture deficit). This article describes an application of TAMSAT-ALERT (Tropical Applications of Meteorology using SATellite data and ground-based observations AgriculturaL EaRly warning sysTem), a decision support tool which exploits the expected persistence of root zone soil moisfall, temperature and wind speed, into soil moisture and other land surface variables by solving the physical equations that govern the various land surface processes. For the top soil layer, any inward flux at the surface is the infiltration, determined from the throughfall (rainfall that is not captured by vegetation), with a maximum infiltration rate set by the soil properties. The soil properties are controlled ture anomalies to provide early warnings of agricultural drought
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