Supraglacial debris affects glacier mass balance as a thin layer enhances surface melting, while a thick layer reduces it. While many glaciers are debris‐covered, global glacier models do not account ...for debris because its thickness is unknown. We provide the first globally distributed debris thickness estimates using a novel approach combining sub‐debris melt and surface temperature inversion methods. Results are evaluated against observations from 22 glaciers. We find the median global debris thickness is ∼0.15 ± 0.06 m. In all regions, the net effect of accounting for debris is a reduction in sub‐debris melt, on average, by 37%, which can impact regional mass balance by up to 0.40 m water equivalent (w.e.) yr‐1. We also find recent observations of similar thinning rates over debris‐covered and clean ice glacier tongues is primarily due to differences in ice dynamics. Our results demonstrate the importance of accounting for debris in glacier modeling efforts.
Plain Language Summary
Many glaciers around the world have a layer of debris (boulders, rocks, and sand) covering the underlying ice over much of the glacier surface, yet global glacier models do not account for debris because the debris thickness is unknown. Here we provide the first estimates of debris thickness for debris‐covered glaciers globally and show the debris substantially reduces regional glacier mass loss. We also find that recent observations that debris‐covered and clean ice glaciers are thinning at similar speeds is primarily due to differences in how glaciers flow. Our results fundamentally advance our ability to account for debris in glacier reconstructions, landscape evolution models, hazard assessments, and glacier projections of glacier runoff and their contribution to sea‐level rise.
Key Points
We produce the first distributed global debris thickness estimates
Accounting for debris significantly reduces regional glacier mass loss
The similar thinning rates of debris‐covered and clean ice glaciers in High Mountain Asia is primarily caused by differences in ice dynamics
SUMMARY
Time-lapse seismic monitoring using full-wavefield methods aims to accurately and robustly image rock and fluid changes within a reservoir. These changes are typically small and localized. ...Quantifying the uncertainty related to these changes is crucial for decision making, but traditional methods that use pixel by pixel uncertainty quantification with large models are computationally infeasible. We exploit the structure of the time-lapse seismic problem for fast wavefield computations using a numerically exact local acoustic solver. This allows us to perform a Bayesian inversion using a Metropolis–Hastings algorithm to sample our posterior distribution. We address the well-known dimensionality problem in global optimization using an image compression technique. We run our numerical experiments using a single shot and a single frequency, however we show that various frequencies converge to different local minima. In addition, we test our framework for both uncorrelated and correlated noise, and we retrieve different histograms for each noise type. Through our numerical examples we show the importance of defining quantities of interest in order to setup an appropriate uncertainty quantification framework involving choosing the number of degrees of freedom and model parametrization that best approximate the problem. To our knowledge, there is no work in the literature studying the time-lapse problem using stochastic full-waveform inversion.
In July 2020, a Mw 7.8 earthquake initiated directly to the east of Simeonof Island offshore of the Alaska Peninsula. The earthquake ruptured the eastern part of the Shumagin Gap, a region devoid of ...large earthquakes over the last century and characterized by low geodetic coupling. Here, we investigate the rupture kinematics of the earthquake using a joint inversion of high‐rate GNSS and strong‐motion data. We find that the rupture was focused between depths of 30–45 km, starting east of the Shumagin Islands and rupturing downdip towards the northwest, with little slip west of 160°W. Early postseismic observations indicate that the entirety of the Shumagin Gap at depths between 40–60 km ruptured with aseismic afterslip and aftershocks. Historically, this earthquake resembles the Shumagin Islands earthquake of 1917, indicating that a possible rupture asperity exists to explain low interseismic coupling and repeating ~M7.5–8 earthquakes.
Plain Language Summary
A large Mw 7.8 earthquake occurred in July 2020 in the Aleutian Islands near a part of the subduction zone that is not locked, the Shumagin Gap. A fully locked fault will be more susceptible to large earthquakes since deformation is not released slowly over time. We model how the earthquake slipped using both observations of displacement and velocity nearby. We find that the July 2020 earthquake ruptured mainly the unlocked portion of the subduction zone and did not rupture into regions that are highly locked. This peculiar pattern of slip was also seen previously in 1917, indicating that the structure of the fault zone in the area may be conducive to earthquakes and some interseismic locking is occurring to allow for M7.5–8 earthquakes every century.
Key Points
The Simeonof Island earthquake ruptured a region of low interseismic coupling in the Shumagin Gap
Early postseismic deformation indicates slip along the whole width of the Shumagin Gap between 40 and 60 km
The Simeonof Island earthquake resembles the 1917 earthquake, indicating a potential rupture asperity within a highly creeping region
SUMMARY
In a recent work, we applied the every earthquake a precursor according to scale (EEPAS) probabilistic model to the pseudo-prospective forecasting of shallow earthquakes with magnitude $M\ ...5.0$ in the Italian region. We compared the forecasting performance of EEPAS with that of the epidemic type aftershock sequences (ETAS) forecasting model, using the most recent consistency tests developed within the collaboratory for the study of earthquake predictability (CSEP). The application of such models for the forecasting of Italian target earthquakes seems to show peculiar characteristics for each of them. In particular, the ETAS model showed higher performance for short-term forecasting, in contrast, the EEPAS model showed higher forecasting performance for the medium/long-term. In this work, we compare the performance of EEPAS and ETAS models with that obtained by a deterministic model based on the occurrence of strong foreshocks (FORE model) using an alarm-based approach. We apply the two rate-based models (ETAS and EEPAS) estimating the best probability threshold above which we issue an alarm. The model parameters and probability thresholds for issuing the alarms are calibrated on a learning data set from 1990 to 2011 during which 27 target earthquakes have occurred within the analysis region. The pseudo-prospective forecasting performance is assessed on a validation data set from 2012 to 2021, which also comprises 27 target earthquakes. Tests to assess the forecasting capability demonstrate that, even if all models outperform a purely random method, which trivially forecast earthquake proportionally to the space–time occupied by alarms, the EEPAS model exhibits lower forecasting performance than ETAS and FORE models. In addition, the relative performance comparison of the three models demonstrates that the forecasting capability of the FORE model appears slightly better than ETAS, but the difference is not statistically significant as it remains within the uncertainty level. However, truly prospective tests are necessary to validate such results, ideally using new testing procedures allowing the analysis of alarm-based models, not yet available within the CSEP.
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