How the International Research Institute for Climate and Society has contributed towards seasonal climate forecast modelling and operations in South Africa

Factors Undermining the Use of Seasonal Climate Forecasts Among Farmers in South Africa and Zimbabwe: Implications for the 1st and 2nd Sustainable Development Goals

The adverse effects of climate change on food production coupled with growing inconsistencies in Indigenous knowledge systems have necessitated some farmers' willingness to rely on seasonal climate forecasts (SCFs) to make informed farming decisions. SCFs provide information regarding the likelihood that the rainfall in the forthcoming season will be higher, lower or normal. While SCFs have scaled up food production among some rural households in Sub-Saharan Africa (SSA), some farmers find it difficult to plug into this vital technology. Failure to utilize SCFs could have severe consequences for household food and nutrition security, especially in semi-arid countries like South Africa and Zimbabwe. By systematically unpacking the literature in South Africa and Zimbabwe from 2011 to 2021, this article seeks to demystify the factors that hamper the use of SCFs in the aforementioned countries. Results indicate that failure to comprehensively understand and interpret probabilistic forecasts as well as ill-timing of forecast dissemination, among others, are factors that undermine the use of SCFs. These issues are discussed both within the broader theoretical debates revolving around ways to dismantle the barriers undermining the use of SCFs in SSA, which could hamper the attainment of both the first and second sustainable development goals.

Remote Sensing and Multi-Criteria Evaluation for Malaria Risk Mapping to Support Indoor Residual Spraying Prioritization in the Central Highlands of Madagascar

The National Malaria Control Program (NMCP) in Madagascar classifies Malagasy districts into two malaria situations: districts in the pre-elimination phase and districts in the control phase. Indoor residual spraying (IRS) is identified as the main intervention means to control malaria in the Central Highlands. However, it involves an important logistical mobilization and thus necessitates prioritization of interventions according to the magnitude of malaria risks. Our objectives were to map the malaria transmission risk and to develop a tool to support the Malagasy Ministry of Public Health (MoH) for selective IRS implementation. For the 2014–2016 period, different sources of remotely sensed data were used to update land cover information and substitute in situ climatic data. Spatial modeling was performed based on multi-criteria evaluation (MCE) to assess malaria risk. Models were mainly based on environment and climate. Three annual malaria risk maps were obtained for 2014, 2015, and 2016. Annual parasite incidence data were used to validate the results. In 2016, the validation of the model using a receiver operating characteristic (ROC) curve showed an accuracy of 0.736; 95% CI [0.669–0.803]. A free plugin for QGIS software was made available for NMCP decision makers to prioritize areas for IRS. An annual update of the model provides the basic information for decision making before each IRS campaign. In Madagascar and beyond, the availability of the free plugin for open-source software facilitates the transfer to the MoH and allows further application to other problems and contexts.

The verification of seasonal precipitation forecasts for early warning in Zambia and Malawi

Abstract. We assess the probabilistic seasonal precipitation forecasts issued by Regional Climate Outlook Forum (RCOF) for the area of two southern African countries, Malawi and Zambia from 2002 to 2013. The forecasts, issued in August, are of rainy season rainfall accumulations in three categories (above normal, normal, and below normal), for early season (October–December) and late season (January–March). As observations we used in-situ observations and interpolated precipitation products from Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP). Differences between results from different data products are smaller than confidence intervals calculated by bootstrap. We focus on below normal forecasts as they were deemed to be the most important for society. The well-known decomposition of Brier score into three terms (Reliability, Resolution, and Uncertainty) shows that the forecasts are rather reliable or well-calibrated, but have a very low resolution; that is, they are not able to discriminate different events. The forecasts also lack sharpness as forecasts for one category are rarely higher than 40 % or less than 25 %. However, these results might be unnecessarily pessimistic, because seasonal forecasts have gone through much development during the period when the forecasts verified in this paper were issued, and forecasts using current methodology might have performed better.