Food and Water Systems in the Intelligent Age 2024

Stack of geospatial data, target region, timeframe Image files TIF, netCDF, etc. Mosaic dataset image cube Digital twin prototype and water manager virtual assistant for the Limpopo River Basin FIGURE 4 Note: This system uses a stack of geospatial data within the Digital Earth Africa Open Data Cube framework. It enables targeted analysis by region and timeframe, with FAIR data practices ensuring accessible and interoperable data management; Source: Mahecha, M. et al. (2020). Earth system data cubes unravel global multivariate dynamics. Earth System Dynamics , vol. 11, pp. 201–234. https://doi.org/10.5194/esd-11-201-2020__;!!Im8kQaqBCw!tBw69sOJXmSkTtEp-Vb0vDptVy5zpR9SHB2BVBrCfAhJi0r3KhtvyB62HIIgpiERHrBog62SmJ_62h3ug 0M05_hWPUD6cjJl$”https://doi.org/10.5194/esd-11-201-2020 .Limpopo River Basin The Limpopo River Basin (LRB), one of the largest in the Southern African Development Community (SADC) region, spans 408,250 kilometres squared (km²) across Botswana, Mozambique, South Africa, and Zimbabwe, supporting 18 million people who depend on its water resources. However, climate variability and overuse have led to severe challenges. Some sections of the river now run dry for 70% of the year, while ongoing droughts strain farmers’ ability to sustain crops and livestock. On the other hand, recurrent floods during the rainy season – or those triggered by tropical cyclones – pose additional challenges for agricultural sustainability and other uses, particularly in Mozambique. Effective, climate-smart water management has become essential to address urban demands, over-extraction and deteriorating water quality, all of which threaten the resilience of small-scale farmers and rural communities. To tackle these challenges, the International Water Management Institute (IWMI), in collaboration with CGIAR Digital Innovation and the Limpopo Watercourse Commission (LIMCOM), has developed a prototype digital twin for the LRB.8 This platform, aligned with the stack framework, integrates 3D models, Earth observation, IoT sensors and field data, providing a holistic view of water dynamics. It includes an AI virtual assistant tool to interrogate and visualize key actionable data and forecasts, offering a holistic view of water dynamics in the basin. Built on FAIR data principles (findability, accessibility, interoperability, and reuse of digital assets) and Open Data Cube practices, the digital twin provides near real-time insights on water availability, ecosystem health and more. It will support evidence-based discussions among the four countries, addressing their diverse priorities while encouraging collaboration. Managing the needs and priorities of four different countries is complex. However, this platform is an important starting point for evidence-based discussions. At the core of this innovative system is an AI- based virtual assistant as part of the decision layer, co-designed with Microsoft Research and LIMCOM and tailored for water managers, researchers and citizens to provide guidance on efficient water practices in the basin. This virtual assistant, powered by GPT-4, allows users to interact with complex datasets through a natural language chat interface. Food and Water Systems in the Intelligent Age 11