Characterizing Long Term Rainfall Data for Estimating Climate Risk in Semi-arid Zimbabwe

Abstract

There is still a strong disconnect between the parameters and scale of information that farmers prefer and those of the seasonal climate forecasts (SCFs). There is a need to augment SCFs as they are currently presented; to make them more useful for farm decision making. The objective of this study was to use simple statistical methods of analysis to characterise long term rainfall for estimating climate risk in semi-arid Zimbabwe. This study reveals the importance of accessing long-term daily rainfall records to enable “weather-within-climate” analyses that can be tailored to the needs of farmers. The most critical point is to describe the climate in terms of events of direct relevance to farming rather than simple standard measures. Agronomically, the important rainfall events relevant to farmers in rainfed agriculture include the start, end and length of the rainy season, risks of dry spells as well as the distribution of rainfall amounts through the year. There are difficult risks in El Nino compared to Ordinary and La Nina seasons in terms of frequency and length of dry spells, number of rain days, rainfall onset and cessation dates and total rainfall amount. The chance of a dry-spell being broken is also considerably lower in El Nino years, compared to La Nina and Ordinary years. Packaging SCF with historic climate data as well as bringing in the shorter range forecasts, together with the experience of the season as it develops is a way in which value could be added to climate information dissemination. Technologies that enhance water use efficiency could also be one of the major areas of research to be integrated into the semi-arid farmers’ existing strategies to cope with climate variability and ultimately change.