Remote sensing, numerical models, and machine learning have been widely used for investigating environmental risks under climate change. It is known that they tend to do an excellent job in mapping, simulating, and projecting the long-term changes in average conditions. However, damages associated with extreme weathers by droughts, floods, forest fires, heat-related mortality, and crop yield loss are often more devastating than those caused by gradual climate changes. How remote sensing, numerical models, and machine learning can be used for assessing the impacts of extreme weathers on the natural and human systems remains uncertain.
This session aims to summarize current progress in assessing the ability of remote sensing, numerical models, and machine learning for quantifying climate risks in multiple sectors, such as water, agriculture, and human health.
We especially welcome investigations focusing on the inter-comparison of methodologies, as well as multi-sectoral, cross-sectoral, and integrated assessments.