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

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

A Theoretical Analysis of the Geography of Schistosomiasis in Burkina Faso Highlights the Roles of Human Mobility and Water Resources Development in Disease Transmission

We study the geography of schistosomiasis across Burkina Faso by means of a spatially explicit model of water-based disease dynamics. The model quantitatively addresses the geographic stratification of disease burden in a novel framework by explicitly accounting for drivers and controls of the disease, including spatial information on the distributions of population and infrastructure, jointly with a general description of human mobility and climatic/ecological drivers. Spatial patterns of disease are analysed by the extraction and the mapping of suitable eigenvectors of the Jacobian matrix subsuming the stability of the disease-free equilibrium. The relevance of the work lies in the novel mapping of disease burden, a byproduct of the parametrization induced by regional upscaling, by model-guided field validations and in the predictive scenarios allowed by exploiting the range of possible parameters and processes. Human mobility is found to be a primary control at regional scales both for pathogen invasion success and the overall distribution of disease burden. The effects of water resources development highlighted by systematic reviews are accounted for by the average distances of human settlements from water bodies that are habitats for the parasite's intermediate host. Our results confirm the empirical findings about the role of water resources development on disease spread into regions previously nearly disease-free also by inspection of empirical prevalence patterns. We conclude that while the model still needs refinements based on field and epidemiological evidence, the proposed framework provides a powerful tool for large-scale public health planning and schistosomiasis management