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We systematically used two approaches to analyze broadband seismic signals for monitoring active volcanoes: one is waveform inversion of very‐long‐period (VLP) signals assuming possible source mechanisms; the other is a source location method of long‐period (LP) events and tremor using their amplitudes. The deterministic approach of the waveform inversion is useful to constrain the source mechanism and location but is basically only applicable to VLP signals with periods longer than a few seconds. The source location method assumes isotropic radiation of S waves and uses seismic amplitudes corrected for site amplifications. This simple approach provides reasonable source locations for various seismic signals such as a VLP event accompanying LP signals, an explosion event, and tremor associated with lahars and pyroclastic flows observed at five or fewer stations. Our results indicate that a frequency band of about 5–12 Hz and a Q factor of about 60 are appropriate for the determination of the source locations. In this frequency band the assumption of isotropic radiation may become valid because of the path effect caused by the scattering of seismic waves. The source location method may be categorized as a stochastic approach based on the nature of scattering waves. Systematic use of these two approaches provides a way to better utilize broadband seismic signals observed at a limited number of stations for improved monitoring of active volcanoes.

Saltwater intrusion is a critical concern for coastal communities due to its impacts on fresh ecosystems and civil infrastructure. Declining recharge and rising sea level are the two dominant drivers of saltwater intrusion along the land‐ocean continuum, but there are currently no global estimates of future saltwater intrusion that synthesize these two spatially variable processes. Here, for the first time, we provide a novel assessment of global saltwater intrusion risk by integrating future recharge and sea level rise while considering the unique geology and topography of coastal regions. We show that nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100, with different dominant drivers. Climate‐driven changes in subsurface water replenishment (recharge) is responsible for the high‐magnitude cases of saltwater intrusion, whereas sea level rise and coastline migration are responsible for the global pervasiveness of saltwater intrusion and have a greater effect on low‐lying areas. Plain Language Summary Coastal watersheds around the globe are facing perilous changes to their freshwater systems. Driven by climatic changes in recharge and sea level working in tandem, sea water encroaches into coastal groundwater aquifers and consequently salinizes fresh groundwater, in a process called saltwater intrusion. To assess the vulnerability of coastal watersheds to future saltwater intrusion, we applied projections of sea level and groundwater recharge to a global analytical modeling framework. Nearly 77% of the global coast is expected to undergo measurable salinization by the year 2100. Changes in recharge have a greater effect on the magnitude of salinization, whereas sea level rise drives the widespread extensiveness of salinization around the global coast. Our results highlight the variable pressures of climate change on coastal regions and have implications for prioritizing management solutions. Key Points First global analysis of future saltwater intrusion vulnerability responding to spatially variable recharge and sea level rise is provided Recharge drives the extreme cases of saltwater intrusion, while sea level rise is responsible for its global pervasiveness Nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100

The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor. Plain Language Summary Earthquake activity at volcanic regions is often monitored to indicate volcanic activity. Identifying the time when the energy radiated from an earthquake source arrives at a seismometer is essential for locating the earthquake, which can be difficult for volcanic earthquakes because of high noise levels, high event rates, and obscured onsets. Previous studies have demonstrated that deep learning can excel in picking the arrival times of regular earthquakes. However, it is unclear how sensitive these detectors are to earthquakes in volcanic regions. Here, we first compile a data set of earthquakes from various volcanoes globally. We then show that existing deep‐learning‐based detectors can miss a large fraction of these earthquakes, especially those without an abrupt change in signal amplitude. We then provide two new models which can better detect volcanic earthquakes than existing models. Our model/workflow can be applied to improve monitoring of volcanic earthquakes globally. Key Points We compile a data set of seismic waveforms from various volcanic regions globally We show that existing deep‐learning phase pickers' performances deteriorate with decreasing earthquake frequency content Our retrained models perform better and are more generalizable for monitoring volcano seismicity, especially long‐period earthquakes

Since the 1919 foundation of the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI), the fields of volcano seismology and acoustics have seen dramatic advances in instrumentation and techniques, and have undergone paradigm shifts in the understanding of volcanic seismo-acoustic source processes and internal volcanic structure. Some early twentieth-century volcanological studies gave equal emphasis to barograph (infrasound and acoustic-gravity wave) and seismograph observations, but volcano seismology rapidly outpaced volcano acoustics and became the standard geophysical volcano-monitoring tool. Permanent seismic networks were established on volcanoes (for example) in Japan, the Philippines, Russia, and Hawai‘i by the 1950s, and in Alaska by the 1970s. Large eruptions with societal consequences generally catalyzed the implementation of new seismic instrumentation and led to operationalization of research methodologies. Seismic data now form the backbone of most local ground-based volcano monitoring networks worldwide and play a critical role in understanding how volcanoes work. The computer revolution enabled increasingly sophisticated data processing and source modeling, and facilitated the transition to continuous digital waveform recording by about the 1990s. In the 1970s and 1980s, quantitative models emerged for long-period (LP) event and tremor sources in fluid-driven cracks and conduits. Beginning in the 1970s, early models for volcano-tectonic (VT) earthquake swarms invoking crack tip stresses expanded to involve stress transfer into the wall rocks of pressurized dikes. The first deployments of broadband seismic instrumentation and infrasound sensors on volcanoes in the 1990s led to discoveries of new signals and phenomena. Rapid advances in infrasound technology; signal processing, analysis, and inversion; and atmospheric propagation modeling have now established the role of regional (15–250 km) and remote (> 250 km) ground-based acoustic systems in volcano monitoring. Long-term records of volcano-seismic unrest through full eruptive cycles are providing insight into magma transport and eruption processes and increasingly sophisticated forecasts. Laboratory and numerical experiments are elucidating seismo-acoustic source processes in volcanic fluid systems, and are observationally constrained by increasingly dense geophysical field deployments taking advantage of low-power, compact broadband, and nodal technologies. In recent years, the fields of volcano geodesy, seismology, and acoustics (both atmospheric infrasound and ocean hydroacoustics) are increasingly merging. Despite vast progress over the past century, major questions remain regarding source processes, patterns of volcano-seismic unrest, internal volcanic structure, and the relationship between seismic unrest and volcanic processes.

Five three‐component broadband ocean bottom seismometers (OBSs) were deployed on the seafloor around the Aeolian Islands (Southern Tyrrhenian Sea). By comparing OBSs digital seismograms, we found a low‐frequency seismicity recorded only at OBS05, the nearest seafloor station to Stromboli volcano. This seismicity appears in the form of a continuous seismic signal (tremor‐like‐signal) as well as a considerable number of shock‐like events. We focused on recordings from OBS05 to verify their correlation with Stromboli volcanic activity. From the spectral analysis, we observed low‐frequency events (LP events), superposed upon the continuous background noise (tremor). LP events and tremor, showing similar energy fluctuations and frequency content, appear to be produced by the same dynamic processes. We interpret this low‐frequency seismicity as probably originating from a continuous uprising of gas bubbles from the deeper part of the Stromboli magmatic column. This could highlight the existence of a deeper source for low‐frequency seismicity.

Several seismic tomographic studies have been carried out to outline the intricate interplay between tectonics and magma uprising at Etna volcano. Most of these studies assume a seismically isotropic crust. Here we employ a novel methodology that accounts for the anisotropic structure of the crust. Anisotropy patterns are consistent with the Etna structural trends, unveiling the depth extent of fault segments. A high‐velocity volume, deepening toward the northwest, identifies the subducting foreland units that appear to confine a low‐velocity anomaly, interpreted as the expression of magmatic fluids within the crust. A discontinuity, likely tectonic in origin, affects the subducting units and allows magma transfer from depth to the surface. This structural configuration may explain the presence of such a very active basaltic strato‐volcano within an atypical collisional geodynamic context. Plain Language Summary [In our study, we investigate the complex relationship between tectonics and magma ascent at Etna volcano. Unlike previous research, we consider the anisotropic nature of the crust, meaning its properties vary depending on direction. Using our innovative methodology, we have uncovered patterns aligned with Etna fault segments, revealing their depth and distribution. We have identified the presence of magma fluids within the crust and potential pathways for its uprising. Our results provides an explanation for the emplacement of the very active Etna basaltic volcano within an unusual collisional geodynamic setting.] Key Points This study conducts the first P‐wave anisotropic tomography using local earthquakes recorded in the area of Etna volcano The tomography resolves three‐dimensional (3D) isotropic and anisotropic structures beneath the volcano Anisotropy reveals geological features providing valuable insights into the interplay between tectonic deformation and volcanic activity

Both volcano‐tectonic (VTs) and deep long‐period earthquakes (DLPs) have been documented at Akutan Volcano, Alaska and may reflect different active processes helpful for eruption forecasting. In this study, we perform high‐resolution earthquake detection, classification, and relocation using seismic data from 2005 to 2017 to investigate their relationship with underlying magmatic processes. We find that the 2,787 VTs and 787 DLPs are concentrated above and below the inferred magma reservoir respectively. They both are clustered as swarms and occur preferentially during inflation episodes with no spatial migrations. However, moment release rates of DLP swarms show a stronger correlation with inflation and their low‐frequency content is likely a source instead of a path effect. Therefore, we infer that DLPs are directly related to unsteady magma movement through a complex pathway. In comparison, repeating events are observed in VTs. Thus, we conclude that they represent fault rupture triggered by magma/fluid movement or larger earthquakes. Plain Language Summary Volcano eruption forecasting is a challenging task that often requires the deciphering of processes underlying observed signs of volcanic unrest. As seismometers become common monitoring sensors on volcanoes, the recorded ground motion is valuable for scientists to study eruption precursors. Earthquakes are commonly observed and generally inferred to be associated with stress perturbations in the shallow crust. However, earthquakes with predominantly lower‐frequency energy are sometimes observed at depth and their origin is enigmatic. In this paper, we use the existing catalog of earthquakes at Akutan Volcano in Alaska between 2005 and 2017 as templates to successfully detect more earthquakes before locating them with higher precision. We find that earthquakes at Akutan Volcano tend to occur in swarms during times when the ground inflates due to magma accumulation beneath the volcano. Some earthquakes have predominantly low‐frequency energy which suggests a different source mechanism compared to regular earthquakes. Furthermore, the largest events are more strongly correlated with surface inflation. Therefore, we conclude that these lower‐frequency earthquakes are more directly related to unsteady magma movement through a complex pathway compared to regular earthquakes which represent fault rupture triggered by magma/fluid movement or larger earthquakes. Key Points Moment release rates of deep long‐period events correlate more strongly with inflation episodes compared to volcano‐tectonic events Akutan deep long‐period earthquakes are likely due to non‐stationary source effects like unsteady magma transport through complex pathways Akutan volcano‐tectonic earthquakes represent fault ruptures triggered by magma/fluid movements or larger earthquakes

Volcanic activity has been shown to affect Earth's climate in a myriad of ways. One such example is that eruptions proximate to surface ice will promote ice melting. In turn, the crustal unloading associated with melting an ice sheet affects the internal dynamics of the underlying magma plumbing system. Geochronologic data from the Andes over the last two glacial cycles suggest that glaciation and volcanism may interact via a positive feedback loop. At present, accurate sea‐level predictions hinge on our ability to forecast the stability of the West Antarctic Ice Sheet, and thus require consideration of two‐way subglacial volcano‐deglaciation processes. The West Antarctic Ice Sheet is particularly vulnerable to collapse, yet its position atop an active volcanic rift is seldom considered. Ice unloading deepens the zone of melting and alters the crustal stress field, impacting conditions for dike initiation, propagation, and arrest. However, the consequences for internal magma chamber dynamics and long‐term eruption behavior remain elusive. Given that unloading‐triggered volcanism in West Antarctica may contribute to the uncertainty of ice loss projections, we adapt a previously published thermomechanical magma chamber model and simulate a shrinking ice load through a prescribed lithostatic pressure decrease. We investigate the impacts of varying unloading scenarios on magma volatile partitioning and eruptive trajectory. Considering the removal of km‐thick ice sheets, we demonstrate that the rate of unloading influences the cumulative mass erupted and consequently the heat released into the ice. These findings provide fundamental insights into the complex volcano‐ice interactions in West Antarctica and other subglacial volcanic settings. Plain Language Summary In regions like West Antarctica, volcanic eruptions occur underneath ice sheets. When hot magma comes in contact with ice, it can accelerate the melting of the ice cover. Beyond this, as climate change causes ice sheets to shrink, the decreasing weight on a volcano may affect its likelihood of erupting. The effects of ice loss above volcanoes on the underlying volcanic activity are not well understood. We conducted computer simulations to explore how gradual ice loss affects magma stored in the Earth's crust. We find that volcanoes beneath shrinking ice sheets are sensitive to the rate at which the ice sheet shrinks. As the ice melts away, the reduced weight on the volcano allows the magma to expand, applying pressure upon the surrounding rock that may facilitate eruptions. Additionally, the reduced weight from the melting ice above also allows dissolved water and carbon dioxide to form gas bubbles, which causes pressure to build up in the magma chamber and may eventually trigger an eruption. Under these conditions, we find that the removal of an ice sheet above a volcano results in more abundant and larger eruptions, which may potentially hasten the melting of overlying ice through complex feedback mechanisms. Key Points During deglaciation, the evolution of a crustal magma chamber beneath kilometers of ice is sensitive to the rate at which ice is removed A critical rate of unloading can trigger additional eruption events Ice unloading expedites the onset of volatile exsolution, with consequences for magma chamber pressurization and eruption size

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI‐based weather models produces medium‐range forecasts within seconds, with a skill similar to state‐of‐the‐art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI‐models toward process‐based evaluations lays the foundation for hazard‐driven applications. We assess the forecast skill of the top‐performing AI‐models GraphCast, Pangu‐Weather and FourCastNet for convective parameters at lead‐times up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu‐Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments. Plain Language Summary Over the past year, several global AI‐based weather models were released and produce a similar quality of forecasts as traditional weather models. AI‐models are very fast and computationally cheap to produce forecasts. The evaluation of AI‐models has largely focused on single atmospheric variables at certain heights. To forecast specific phenomena, such as thunderstorms, a combination of variables must be accurate at multiple heights. Here we use the output of AI‐models to derive thunderstorm‐related ingredients. We compare 10‐day‐forecasts between the AI‐models GraphCast, Pangu‐Weather and FourCastNet and a state‐of‐the‐art traditional weather model while using a reanalysis data set as the reference. The example of a tornado outbreak in the southern United States shows that all models are capable of forecasting thunderstorm ingredients multiple days in advance. To obtain a robust assessment, we evaluate the entire thunderstorm season in 2020 in North America, Europe, Argentina, and Australia, where severe thunderstorms occur frequently. Two of the three AI‐models achieve similar or even better results than the traditional weather model while being much cheaper to operate computationally. Forecasting thunderstorm parameters directly, instead of calculating them afterward, is likely to produce even better results. This opens opportunities for rapid and accessible forecasts for severe thunderstorm phenomena. Key Points AI‐based global weather models produce forecasts with sufficient accuracy to derive instability and shear metrics skillfully The best AI‐based weather models are capable of competing with state‐of‐the‐art numerical weather predictions of instability and shear This is a major step toward computationally inexpensive and fast convective outlooks

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered. Key Points More than half of the structures in the conterminous United States are exposed to potentially devastating natural hazards Growth rates in hazard hotspots exceed the national trend Risk assessments can be improved by considering multiple hazards, mitigation history and fine‐scale data on the built environment