Research briefs

A new scientific mechanism for assessing the potential risk to worldwide mineral commodity supplies from seismic activity has been developed by USGS scientists.

Researchers have proposed deep learning models that predict droughts using climate data. Long-term forecasts of this kind are sought by agricultural producers planning their operations, by insurance companies and banks evaluating climate risks.

Ten years of data preceding a rockfall in the French Alps suggest the need for more comprehensive monitoring systems.

To better predict long-term flooding risk, scientists at the Department of Energy's Oak Ridge National Laboratory developed a 3D modeling framework that captures the complex dynamics of water as it flows across the landscape.

Scientists from The University of Manchester, The University of Aberdeen and Newcastle University have found that the restoration of upland peatlands is a highly effective strategy for reducing downstream flooding.

This week, large areas of Ontario experienced severe flooding that caused widespread power outages, water damages and disruption. Severe rainfall events are not new, but they are becoming more frequent and costly due to human-caused climate change.

A new global assessment of scientific literature led by researchers at the University of Massachusetts Amherst finds that nature-based solutions (NbS) are an economically effective method to mitigate risks.

Their update accounts for the effects of overlooked physical properties in ice.