Abstract:
The challenge of meeting the growing demands for water, energy, and food is further complicated by the impact of climate and land use and land cover (LULC) change. The Mpumalanga Province where agricultural production compete with coal mining for land and water consumption is a prime example of challenges involved in sustaining the water, food, and energy. A more holistic understanding of LULC can help in managing competing land use objectives, leading to improved climate change adaptation strategies. The Water-Energy-Food (WEF) nexus is an effort to address challenges affecting WEF sectors by taking into consideration the inter-relatedness and interdependencies between these sectors to balance their perspectives and management. As a result, this study aimed to undertake a pragmatic approach that is based on geospatial analytical methods to support WEF nexus climate change adaptation in South Africa’s Mpumalanga Province. To achieve this, the study investigated the impact of LULC change on WEF resources by analyzing South African National Land Cover (SANLC) data from 1990, 2014, 2018 and 2020 using Geographical Information Systems (GIS) and remote sensing techniques. The study also located the recently completed and ongoing adaptation projects that contribute to the WEF nexus in the study area. As such, the logistic regression model was implemented using three scenarios to understand the drivers of the location of the WEF nexus-based climate change adaptation interventions or actions spanning from environmental to socio-economic drivers. Scenario 1: Model based on environmental variables only; Scenario 2: socio-economic variables only and scenario 3: combining environmental and socio-economic variables. Based on the understanding of the drivers and spatial estimation, a framework or model was developed to synthesize and prioritize potential areas of climate change adaptation intervention or action in Mpumalanga Province. The results of LULC change over the study period (1990 – 2020) show that the LULC areas under agriculture, built-up areas, mines and quarries increased from 18.84%, 2.33%, 0.61% in 1990 to 23.73%, 3.41% and 0.79% in 2020, respectively. While grasslands have decreased from 37.36% in 1990 to 30.39% in 2020. All of these changes have a direct impact on water supplies, energy sources, and food production. It was evident that more interventions were associated with areas of extreme climatic variables (e.g., drought related). All models were statistically significant, with Area Under the Curve (AUC) = 72% for scenario 1; 67% for scenario 2 and 73% for scenario 3. The results of the spatial estimation revealed that the northeastern region and northwestern region of the Mpumalanga Province should be prioritized for adaptation interventions. This work provided a broader view of the impact of resource use and management on the overall environment and societal well-being. The results obtained from this study indicate that the use of geospatial tools can be beneficial in the planning and prioritization of activities related to climate change adaptation.
