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Landslides, earthquakes and other natural disasters are expected to increase in the Arctic, yet our ability to make informed decisions about safety is tightly limited by lack of data. As part of the Integrated Arctic Observation System (INTAROS) project, geophones were installed by residents in Greenland and by University of Bergen in Svalbard in 2018. The purpose of the installations was to explore challenges and benefits of community-based data collection for seismological monitoring in the Arctic region. Raspberry Shake units with one/three-component velocity sensors were selected for the deployment, due to their user-friendly configuration, easy installation, and well established digital platform and web services. The purpose of engaging community members in the use of geophone sensors was to monitor earthquakes, cryoseisms (events generated by ice mass), and landslides. We report our findings with respect to challenges regarding the installation and operation of the Raspberry Shake sensors at both locations. Connecting community-based recordings with permanent seismological networks improved both the detection capability and the data support for understanding seismic events in Greenland. In contrast, finding suitable locations for deployments in Longyearbyen turned out to be challenging, because most buildings are constructed on poles due to the permafrost and indoor space is expensive. Promoting citizen seismology in the Arctic could improve monitoring of seismic events in the Arctic while simultaneously raising community awareness of natural hazards.

Earthquake environmental effects (EEEs) were compiled for the earthquakes of 1626, 1759, 1819, and 1904 in the Fennoscandian Peninsula, northern Europe. The principal source of information was the contemporary newspaper press. Macroseismic questionnaires collected in 1759 and 1904 were also consulted. We prepared maps showing newly discovered EEEs together with previously known EEEs and analyzed their spatial distribution. We assigned intensities based on the 2007 Environmental Seismic Intensity (ESI) scale to 27 selected localities and compared them to intensities assigned based on the 1998 European Macroseismic Scale. While the overall agreement between the scales is good, intensities may remain uncertain due to the sparsity of written documentation. The collected data sets are most probably incomplete but still show that EEEs are not unprecedented cases in the target region. The findings include landslides and rockfalls as well as cascade effects with a risk potential and widespread water movements up to long distances. The winter earthquake of 1759 cracked ice over a large area. This investigation demonstrates that the ESI scale also has practical importance for regions with infrequent EEEs.

We apply here a forecasting model to the Italian region for the spatio-temporal distribution of seismicity based on a smoothing Kernel function, Coulomb stress variations, and a rate-and-state friction law. We tested the feasibility of this approach, and analyzed the importance of introducing time-dependency in forecasting future events. The change in seismicity rate as a function of time was estimated by calculating the Coulomb stress change imparted by large earthquakes. We applied our approach to the region of Italy, and used all of the cataloged earthquakes that occurred up to 2006 to generate the reference seismicity rate. For calculation of the time-dependent seismicity rate changes, we estimated the rate-and-state stress transfer imparted by all of the ML≥4.0 earthquakes that occurred during 2007 and 2008. To validate the results, we first compared the reference seismicity rate with the distribution of ML≥1.8 earthquakes since 2007, using both a non-declustered and a declustered catalog. A positive correlation was found, and all of the forecast earthquakes had locations within 82% and 87% of the study area with the highest seismicity rate, respectively. Furthermore, 95% of the forecast earthquakes had locations within 27% and 47% of the study area with the highest seismicity rate, respectively. For the time-dependent seismicity rate changes, the number of events with locations in the regions with a seismicity rate increase was 11% more than in the regions with a seismicity rate decrease.