Climate change effects and increasing resource demands make it difficult for decisionmakers to implement sustainable strategies to ensure access to water, energy, and food. The Climate, Land, Energy and Water systems (CLEWs) framework is widely used to analyze highly interconnected systems. CLEWs facilitates informed decision-making and supports sustainable planning by representing the interlinkages between these systems and their contribution to climate change. Literature highlights that there is a lack of functional tools to process detailed land and water data for developing the CLEWs model without increasing computational complexity. This thesis presents GeoCLEWs, an open source Python-based tool for reproducible processing of high-resolution land and water data to enhance regional and national CLEWs modelling. GeoCLEWs is openly accessible on GitHub and provides automated data collection, preparation, analysis, and statistics generation, which facilitate efficiently the CLEWs model-building process.