Determining spatial variability of dry spells: a Markov-based method, applied to the Makanya catchment, Tanzania

With a growing world population and a trend towards more resource-intensive diets, pressure on land and water resources for food production will continue to increase in the coming decades. Large parts of the world rely on rainfed agriculture for their food security. In Africa, 90 % of the food production is from rainfed agriculture, generally with low yields and a high risk of crop failure. One of the main reasons for crop failure is the occurrence of dry spells during the growing season. Key indicators are the critical dry spell duration and the probability of dry spell occurrence. In this paper a new Markov-based framework is presented to spatially map the length of dry spells for fixed probabilities of non-exceedance. The framework makes use of spatially varying Markov coefficients that are correlated to readily available spatial information such as elevation and distance to the sea. The dry spell map thus obtained is compared to the spatially variable critical dry spell duration, based on soil properties and crop water requirements, to assess the probability of crop failure in different locations. The results show that in the Makanya catchment the length of dry spell occurrence is highly variable in space, even over relatively short distances. In certain areas the probability of crop failure reaches levels that make rainfed agricultural unsustainable, even close to areas where currently rainfed agriculture is successfully being practised. This method can be used to identify regions that are vulnerable to dry spells and, subsequently, to develop strategies for supplementary irrigation or rainwater harvesting.