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We use GPS data to show synchronization between the 2011 and 2016 drought cycle in California, accelerated uplift of the Sierra Nevada Mountains, and enhanced magmatic inflation of the Long Valley Caldera (LVC) magmatic system. The drought period coincided with faster uplift rate, changes in gravity seen in the Gravity Recovery and Climate Experiment (GRACE), and changes in standardized relative climate dryness index. These observations together suggest that the Sierra Nevada elevation is sensitive to changes in hydrological loading conditions, which subsequently influences the LVC magmatic system. We use robust imaging of horizontal GPS velocities to derive time‐variable shear and dilatational strain rates in a region with highly variable station distribution. The results show that the highest strain rates are near the eastern margin of the Sierra Nevada and western edge of the Central Walker Lane (CWL) passing directly through LVC. The drought period saw geographic shifts in the distribution in active shear strain in the CWL more than 60 km from the LVC, delineating the minimum extent over which the active magmatic system affects the CWL tectonic environment. We analyze declustered seismicity data to show that locations with higher seismicity rates tend to be (1) areas with higher strain rates and (2) areas in which strain rates increased during drought‐enhanced inflation. We hypothesize that drought conditions reduce vertical surface mass loading, which decreases pressure at depth in the LVC system, in turn enhances magmatic inflation, and drives horizontal elastic stress changes that redistribute active CWL strain and modulate seismicity. Plain Language Summary: We show that there were connections between the recent drought in California to changes in uplift rate of the Sierra Nevada, magmatic inflation at the Long Valley Caldera near Mammoth, CA, and earthquakes in the region. We use GPS, seismic, gravity, and climate data to show that the speed of uplift of the Sierra Nevada is sensitive to big changes in surface and groundwater (e.g., in snow, streams, lakes, and aquifers) and that these changes correlate with movements within the large volcano. A plausible explanation is that the weight of snow and water loads the surface and changes pressures deep in the Earth. As the Sierra Nevada was unloaded during recent drought, there was less pressure on the magmatic system, allowing fluids to rise or expand more easily. When that occurred, it provided an outward horizontal push on the crust, influencing small‐ to moderate‐sized earthquakes up to 60 km away. We show where and when these changes occurred and specific examples of earthquakes that may have been triggered by this inflation. Key Points: Faster Sierra Nevada uplift during the 2011–2016 drought in California coincided with faster magmatic inflation at the Long Valley CalderaFaster magmatic inflation changed the rate and distribution of crustal strain in the Central Walker Lane, >60 km from Long ValleySeismicity patterns in the Central Walker Lane suggest that long‐term crustal strain may be driven in part by Long Valley magmatic inflation [ABSTRACT FROM AUTHOR]

This paper introduces a statistical technique, based on the recently developed Multiscale Trend Analysis (MTA), for quantifying correlations between non-stationary processes observed at irregular non-coincident time grids. We apply this technique to studying the temporal correlation between the dynamics of the ductile and brittle layers in the lithosphere. Our results confirm the previously reported strong positive correlation between the codaQ-1 and seismicity and its drop before major earthquakes observed in California. The proposed technique has significant advantages over the conventional correlation analysis: (1) MTA allows one to work directly with non-coincident time series without preliminary resampling the data; (2) the correlation is defined via the stable objects-trends-rather than noisy individual observations, hence it is highly robust; (3) the correlations are quantified at different time scales. The suggested technique seems promising for the wide range of applied problems dealing with coupled time series. [ABSTRACT FROM AUTHOR]

Abstract: Short-term earthquake prediction, months in advance, is an elusive goal of earth sciences, of great importance for fundamental science and for disaster preparedness. Here, we describe a methodology for short-term prediction named RTP (Reverse Tracing of Precursors). Using this methodology the San Simeon earthquake in Central California (magnitude 6.5, Dec. 22, 2003) and the Tokachi-Oki earthquake in Northern Japan (magnitude 8.1, Sept. 25, 2003) were predicted 6 and 7 months in advance, respectively. The physical basis of RTP can be summed up as follows: An earthquake is generated by two interacting processes in a fault network: an accumulation of energy that the earthquake will release and a rise of instability triggering this release. Energy is carried by the stress field, instability is carried by the difference between the stress and strength fields. Both processes can be detected and characterized by “premonitory” patterns of seismicity or other relevant fields. Here, we consider an ensemble of premonitory seismicity patterns. RTP methodology is able to reconstruct these patterns by tracing their sequence backwards in time. The principles of RTP are not specific to earthquakes and may be applicable to critical transitions in a wide class of hierarchical non-linear systems. [Copyright &y& Elsevier]