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In this paper we document the first example‐the southern Delaware Basin‐where widespread induced triggered (as opposed to “driven”) seismicity across a large area exhibits a maximum magnitude truncation. The most likely cause of this truncation is that although the shallow faults in this area are many km in length, they are structurally constrained and have limited down‐dip widths, typically no more than approximately 1 km. Ruptures on faults of such limited width are not expected to attain high aspect ratios. As such, the ruptures on such faults would be limited to dimensions of roughly 1 km2, which corresponds to magnitudes which closely match the observed MMAX truncation. The MMAX truncation for induced earthquakes in the southern Delaware Basin has significant implications since it implies that down‐dip fault widths may play an important role in constraining the maximum magnitudes of induced events.

Earthquake magnitude‐frequency distributions exhibit significant space‐time variations, which can provide critical insights into the physical processes driving seismicity. Understanding these variations is crucial for assessing seismic hazards and uncovering the physical processes driving earthquakes. One key parameter, the b‐value, describes the relative proportion of small to large earthquakes and is thought to reflect factors such as stress conditions and fault properties. However, empirical evidence linking b‐value variations to physical processes in real tectonic settings is still limited. Here, we show that b‐value is systematically higher in regions with elevated heat flow, consistently across different tectonic settings and faulting style. This suggests that thermal conditions play a fundamental role in controlling earthquake size distributions, controlling the likelihood of large earthquakes in the different areas.

The Russian heatwave in 2010 killed tens of thousands of people, and was by far the worst event in Europe since at least 1950, according to recent studies and a novel universal heatwave index capturing both the duration and magnitude of heatwaves. Here, by taking an improved version of this index, namely the heat wave magnitude index daily, we rank the top ten European heatwaves that occurred in the period 1950–2014, and show the spatial distribution of the magnitude of the most recent heatwave in summer 2015. We demonstrate that all these events had a strong impact reported in historical newspapers. We further reveal that the 1972 heatwave in Finland had a comparable spatial extent and magnitude as the European heatwave of 2003, considered the second strongest heatwave of the observational era. In the next two decades(2021–2040), regional climate projections suggest that Europe experiences an enhanced probability for heatwaves comparable to or greater than the magnitude, extent and duration of the Russian heatwave in 2010. We demonstrate that the probability of experiencing a major European heatwave in the coming decades is higher in RCP8.5 than RCP4.5 even though global mean temperature projections do not differ substantially. This calls for a proactive vulnerability assessment in Europe in support of formulating heatwave adaptation strategies to reduce the adverse impacts of heatwaves.

Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning. Plain Language Summary Distributed Acoustic Sensing (DAS) is an emerging technique that can convert an optical fiber cable into a dense array to record seismic waves from earthquakes. The recorded seismic signals contain essential information about earthquakes. For example, DAS can record high‐amplitude signals from earthquakes with large magnitudes. However, the exact setting of the optical cables (i.e., installation conditions and coupling with the surrounding medium) is often unknown, thus preventing quantitative estimations of earthquake magnitudes with DAS. In this study, we analyze earthquake data recorded by different DAS arrays and develop a data‐driven method to obtain an empirical relation between the earthquake magnitude and the amplitude of DAS signals. We show that this empirical relation can accurately estimate the earthquake magnitude directly from the DAS data. Furthermore, the empirical relation we obtain from one area can also be applied to new regions with slight calibrations. Our empirical relation can significantly expand the applications of DAS in earthquake research, such as seismic hazard assessment and earthquake early warning. Key Points We present the first data‐based scaling relation between Distributed Acoustic Sensing (DAS) amplitude and earthquake magnitude Earthquake magnitude can be reliably estimated from DAS amplitude with the scaling relation The DAS scaling relation can be transferred from one region to another with minor calibrations

Magnitude is a measure of the size of an earthquake. In practical work such as seismic activity analysis, people usually think that an earthquake has only one magnitude. Many people will have such doubts：why use multiple magnitudes? Why magnitude can’t be unified? In earthquake monitoring, why magnitudes can’t be converted to each other? Why are different magnitudes of the same earthquake different? How to use multiple magnitudes? This paper describes the principle of magnitude determination and the cleverness of magnitude, and discusses the above 11 problems so that scientific researchers and managers can correctly determine and use magnitude in practical work.

The K2 mission will make use of the Kepler spacecraft and its assets to expand upon Kepler's groundbreaking discoveries in the fields of exoplanets and astrophysics through new and exciting observations. K2 will use an innovative way of operating the spacecraft to observe target fields along the ecliptic for the next 2-3 years. Early science commissioning observations have shown an estimated photometric precision near 400 ppm in a single 30 minute observation, and a 6-hr photometric precision of 80 ppm (both at V = 12). The K2 mission offers long-term, simultaneous optical observation of thousands of objects at a precision far better than is achievable from ground-based telescopes. Ecliptic fields will be observed for approximately 75 days enabling a unique exoplanet survey which fills the gaps in duration and sensitivity between the Kepler and TESS missions, and offers pre-launch exoplanet target identification for JWST transit spectroscopy. Astrophysics observations with K2 will include studies of young open clusters, bright stars, galaxies, supernovae, and asteroseismology.

Astrometric surveys such as Gaia and LSST will measure parallaxes for hundreds of millions of stars. Yet they will not measure a single distance. Rather, a distance must be estimated from a parallax. In this didactic article, I show that doing this is not trivial once the fractional parallax error is larger than about 20%, which will be the case for about 80% of stars in the Gaia catalog. Estimating distances is an inference problem in which the use of prior assumptions is unavoidable. I investigate the properties and performance of various priors and examine their implications. A supposed uninformative uniform prior in distance is shown to give very poor distance estimates (large bias and variance). Any prior with a sharp cut-off at some distance has similar problems. The choice of prior depends on the information one has available-and is willing to use-concerning, e.g., the survey and the Galaxy. I demonstrate that a simple prior which decreases asymptotically to zero at infinite distance has good performance, accommodates nonpositive parallaxes, and does not require a bias correction.

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τ c and P d methods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (M JMA ) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records.

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

Humans and nonhuman animals share the capacity to estimate, without counting, the number of objects in a set by relying on an approximate number system (ANS). Only humans, however, learn the concepts and operations of symbolic mathematics. Despite vast differences between these two systems of quantification, neural and behavioral findings suggest functional connections. Another line of research suggests that the ANS is part of a larger, more general system of magnitude representation. Reports of cognitive interactions and common neural coding for number and other magnitudes such as spatial extent led us to ask whether, and how, nonnumerical magnitude interfaces with mathematical competence. On two magnitude comparison tasks, college students estimated (without counting or explicit calculation) which of two arrays was greater in number or cumulative area. They also completed a battery of standardized math tests. Individual differences in both number and cumulative area precision (measured by accuracy on the magnitude comparison tasks) correlated with interindividual variability in math competence, particularly advanced arithmetic and geometry, even after accounting for general aspects of intelligence. Moreover, analyses revealed that whereas number precision contributed unique variance to advanced arithmetic, cumulative area precision contributed unique variance to geometry. Taken together, these results provide evidence for shared and unique contributions of nonsymbolic number and cumulative area representations to formally taught mathematics. More broadly, they suggest that uniquely human branches of mathematics interface with an evolutionarily primitive general magnitude system, which includes partially overlapping representations of numerical and nonnumerical magnitude.