Access to sufficient freshwater is crucial for humans and ecosystems, yet two to three billion people already suffer from shortages — a number likely to rise with population growth and climate change. Understanding how water demand increases, availability declines, and water use affects societies and ecosystems is vital for developing adaptation strategies. However, simulating future global-change scenarios remains computationally demanding. Large-scale water resource models are increasingly complex, making it difficult to run multiple scenarios or test model sensitivities due to high computational costs. This limits our capacity to assess uncertainty, adaptation options, and long-term sustainability under global change.
This project aims to overcome these limitations by developing an open-source, GPU-accelerated, high-resolution modelling toolkit that dynamically links groundwater, surface water, and crop-growth processes at the global scale. By optimizing computational bottlenecks, we will enable extensive scenario and sensitivity analyses to improve global water, food, and ecosystem assessments and support climate-resilient water management worldwide.
