Managing water usage is crucial for sustainable development and economic growth, as water is a limited resource. To help plan for the future, researchers created a detailed dataset of scenarios of global water usage from 2010 to 2100. This dataset considers various factors, such as climate and socioeconomic changes, and breaks down water usage by sectors such as agriculture, industry, and domestic use. By providing a comprehensive view of water usage at a fine scale, this dataset can help researchers understand the challenges of water scarcity and inform decision-making for sustainable water management.
This groundbreaking research presents a high-resolution dataset mapping global water usage scenarios from 2010 to 2100, offering insights into future changes due to climate change and socioeconomic development. The first of its kind, this work provides detailed information on water usage across sectors, including 13 crop types, at fine resolution in both space and time. Crucial for addressing water scarcity and sustainable development, this comprehensive dataset enables scientists and policymakers to understand regional water use patterns and effectively manage resources. The research explores dynamic interactions among energy, water, and land systems globally by innovatively combining multiple models and datasets. The dataset serves as a valuable tool for scientists in agriculture, climate change, and resource management to investigate the effects of water scarcity and develop sustainable solutions.
Researchers developed a global monthly gridded sectoral water withdrawal and consumption dataset at 0.5° resolution for 2010–2100 across 75 diverse scenarios. This dataset is scientifically significant, as it globally quantifies the sources of demand-side pressures on scarce water resources under various future socioeconomic and climate scenarios. The study accounts for key factors such as water use for human activities, a fine spatiotemporal scale, and detailed irrigation water use by crop types. The dataset was generated by coupling the Global Change Analysis Model with a land use spatial downscaling model (Demeter), a global hydrologic framework (Xanthos), and a water withdrawal downscaling model (Tethys). The dataset offers a finer spatiotemporal resolution for future projections compared to previous studies and provides more detail in the irrigation sector, including distinct representations of 13 different crop types. This research addresses the critical need for future projections of distributed water demand at a fine resolution, and the dataset can be downloaded from an open-source online repository.