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Ice jams that occurred on the Pemigewasset River in central New Hampshire resulted in significant localized flooding on 26 February 2017 and 13 January 2018. Analyses of these two case studies shows that both ice jam events occurred in association with enhanced moisture transport characteristic of atmospheric rivers (ARs) that resulted in rain-on-snow, snowpack ablation, and rapid increases in streamflow across central New Hampshire. However, while the ice jams and ARs that preceded them were similar, the antecedent hydrometeorological characteristics of the region were different. The February 2017 event featured a “long melting period with low precipitation” scenario, with several days of warm (~5°–20°C) maximum surface temperatures that resulted in extensive snowmelt followed by short-duration, weak AR that produced ~10–15 mm of precipitation during a 6-h period prior to the formation of the ice jam. Alternatively, the January 2018 event featured a “short melting period with high precipitation” scenario with snowmelt that occurred primarily during a more intense and long-duration AR that produced >50 mm of rainfall during a 30-h period prior to the formation of the ice jam. Composite analysis of 20 ice jam events during 1981–2019 illustrates that 19 of 20 events were preceded by environments characterized by ARs along the U.S. East Coast and occur in association with a composite corridor of enhanced integrated water vapor > 25 mm collocated with integrated water vapor transport magnitudes > 600 kg m−1 s−1. Additional analyses suggest that most ice jams on the Pemigewasset River share many common synoptic-scale antecedent meteorological characteristics that may provide situational awareness for future events.

Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive., IMPORTANCE Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.