Monitoring progress of the Sendai Framework using a geospatial model: The example of people affected by agricultural droughts in Eastern Cape, South Africa

• Published in: Progress in disaster science 2020-01, Vol.5, p.100062, Article 100062
• Main Authors: Walz, Yvonne, Min, Annika, Dall, Karen, Duguru, Moses, Villagran de Leon, Juan-Carlos, Graw, Valerie, Dubovyk, Olena, Sebesvari, Zita, Jordaan, Andries, Post, Joachim
• Format: Article
• Language: English
• Subjects: Affected people; Disaster risk reduction; Drought; Monitoring; Sendai Framework; South Africa
• ISSN: 2590-0617; 2590-0617
• DOI: 10.1016/j.pdisas.2019.100062

The Sendai Framework for Disaster Risk Reduction (SFDRR) was adopted by 187 countries and offers a tangible agenda for evidence-based policy for disaster risk reduction as an integral part of the overall post-2015 global development agenda. The progress of implementation of the seven Global Sendai Targets at the national level is tracked by a set of 38 indicators. However, despite the formal commitment, the majority of countries is currently not in the position to monitor the implementation of the Global Targets. The lack of information on disaster-related loss and damage is mainly due to gaps in data availability, quality, and accessibility, which impairs an accurate, timely and high quality monitoring process. This research addressed this gap by developing a model approach, which aimed at “translating” indicators described by the technical guidance of the United Nations Office for Disaster Risk Reduction (UNDRR) into a geospatial procedure which builds on remote sensing data, climate data, land cover and land use data, agricultural statistics and population census data. With this geospatial model approach, we quantified indicators of the SFDRR for Target B “number of people affected” for the example of agricultural drought in the Eastern Cape province of South Africa in a spatially explicit way. We conducted a media content analysis to generate proxy reference data for evaluation of the model results. In addition, we explored the sensitivity of the model using three different input data on drought hazard, namely the Vegetation Condition Index (VCI), the Standard Precipitation Evapotranspiration Index (SPEI) and the combination of these. The spatial distribution of number of people affected corresponded very well with reference data from the media content analysis; however, model results were very sensitive to different hazard input data. This geospatial model based on remote sensing and geostatistical data is to the best of our knowledge the first attempt to measure Sendai indicators in the absence of national loss and damage databases and provides a unique opportunity to support many countries in implementing the framework. Due to its retrospective nature, even a baseline measure of the indicators can be derived as a reference for monitoring progress. However, the model needs to be further validated in order to qualify the underlying assumptions made to determine thresholds of people being affected. Future research should transfer this model to differen