Search Results (2)

This research utilized the bagging machine learning algorithm along with the Thornthwaite moisture index (TMI) to enhance the understanding of climate variability and change, with the objective of identifying the most efficient climate service pathways in Sub-Saharan Africa (SSA). Monthly datasets at a 0.5° resolution (1960–2020) were collected and analyzed using R 4.2.2 software and spreadsheets. The results indicate significant changes in climatic conditions in Sudan, with aridity escalation at a rate of 0.37% per year. The bagging algorithm illustrated that actual water use was mainly influenced by rainfall and runoff management, showing an inverse relationship with increasing air temperatures. Consequently, sustainable strategies focusing on runoff and temperature control, such as rainwater harvesting, agroforestry and plant breeding were identified as the most effective climate services to mitigate and adapt to climate variability in SSA. The findings suggest that runoff management (e.g., rainwater harvesting) could potentially offset up to 22% of the adverse impacts of climate variability, while temperature control strategies (e.g., agroforestry) could account for the remaining 78%. Without these interventions, climate variability will continue to pose serious challenges to food security, livelihood generations, and regional stability. The research calls for further in-depth studies on the attributions of climate variability using finer datasets. Full article

Expansive soils go through significant volume changes due to seasonal moisture variations resulting in ground movements. The ground movement related problems are likely to worsen in the future due to climate change. It is important to understand and incorporate likely future changes in design to ensure the resilience of structures built on such soils. However, there has been a limited amount of work quantifying the effect of climate change on expansive soils movement and related behaviour of structures. The Thornthwaite Moisture Index (TMI) is one of the commonly used climate classifiers in quantifying the effect of atmospheric boundary on soil behaviour. Using the long-term weather data and predicted future changes under different emission scenarios, a series of TMI maps are developed for South Australia. Potential changes in ground movement are then estimated for a selected area using a simplified methodology where the effect of future climate is captured through changes in TMI. Results indicate that South Australia is likely to face a significant reduction in TMI under all emission scenarios considered in this study. The changes in TMI will lead to a considerable increase in potential ground movement which will influence the behaviour of structures built on them and in some areas may lead to premature failure if not considered in the design. Full article