Linking Satellite, Land Capability, and Socio-Economic Data for Local-Level Climate-Change-Adaptive Capacity Assessments and Decision Support

Abstract

Climate change is now one of the most formidable threats to the livelihoods of resource-poor communities in low-income developing countries world-wide. Addressing this challenge continues to be undermined by the conspicuous absence of actionable adaptation strategies that are potentially capable of enhancing our capacities to realize the Millennial Sustainable Development Goals that seek to securitize access to adequate food supplies for everybody. This paper attempts to address this limitation by providing an improvised geostatistical methodology that integrates multi-source data to map the adaptive capacities of vulnerable communities in a selected South African local municipality, whose livelihoods are largely dependent on rain-fed agriculture. The development of this methodology was based on the use scripts that were compiled in Python and used to test-try its usefulness through a case-study-based assessment of the climate-change-adaptive capacities of local communities in Raymond Mhlaba Local Municipality (RMLM), Eastern Cape Province, South Africa. A Bayesian maximum entropy framework-based technique was used to overcome the lack of missing soil moisture data, which we included because of its reliable usefulness as a surrogate indicator of climate-change-driven variations in this variable on the sustainability of rain-fed agriculture. Analysis of the results from a sampling universe of 124 communities revealed that 65 and 56 of them had high and medium adaptive capacities, respectively, with the remaining 3 having low adaptive capacities. This finding indicates that more than half of the communities in the municipality’s communities have limited capabilities to cope with climate change’s impacts on their livelihoods. Although our proposed methodology is premised on findings from a case-study-based investigation, it is still extremely useful because it demonstratively shows that there is tremendous scope for the scientific community to provide objectively informed insights that can be used to enhance the adaptive capacities of those in need of the badly needed but difficult-to-access information. Added to this is the fact that our proposed methodology is not only applicable for use under different environmental settings but also capable of allowing us to cost-effectively tap into the rich, wide-ranging, freely accessible datasets at our disposal. The aim of this submission is to show that although we have the information, we need to address these persevering challenges by exploring innovative approaches to translate the knowledge we have into actionable climate-change-adaptation strategies.