Influences of crustal thickening in the Tibetan Plateau on loading modeling and inversion associated with water storage variation

We use the average crustal structure of the CRUST1.0 model for the Tibetan Plateau to establish a realistic earth model termed as TC1P, and data from the Global Land Data Assimilation System (GLDAS) hydrology model and Gravity Recovery and Climate Experiment (GRACE) data, to generate the hydrology signals assumed in this study. Modeling of surface radial displacements and gravity variation is performed using both TC1P and the global Preliminary Reference Earth Model (PREM). Furthermore, inversions of the hydrology signals based on simulated Global Positioning System (GPS) and GRACE data are performed using PREM. Results show that crust in TC1P is harder and softer than that in PREM above and below a depth of 15 km, respectively, causing larger differences in the computed load Love numbers and loading Green's functions. When annual hydrology signals are assumed, the differences of the radial displacements are found to be as large as approximately 0.6 mm for the truncated degree of 180; while for hydrology-trend signals the differences are very small. When annual hydrology signals and the trends are assumed, the differences in the surface gravity variation are very small. It is considered that TC1P can be used to efficiently remove the hydrological effects on the monitoring of crustal movement. It was also found that when PREM is used inappropriately, the inversion of the hydrology signals from simulated annual GPS signals can only recover approximately 88.0% of the annual hydrology signals for the truncated degree of 180, and the inversion of hydrology signals from the simulated trend GPS signals can recover approximately 92.5% for the truncated degree of 90. However, when using the simulated GRACE data, it is possible to recover almost 100%. Therefore, in future, the TC1P model can be used in the inversions of hydrology signals based on GPS network data. PREM is also valid for use with inversions of hydrology signals from GRACE data at resolutions of approximately 220 km and larger.published_or_final_versio

Water storage changes in North America retrieved from GRACE gravity and GPS data

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The Influence of Sediments, Lithosphere and Upper Mantle (Anelastic) With Lateral Heterogeneity on Ocean Tide Loading and Ocean Tide Dynamics

Ocean tide loading (OTL) and ocean tide dynamics (OTD) are known to be affected by Earth's internal structures, with the latter being affected by the self-attraction and loading (SAL) potential. Combining the 3D earth models Lyon and LITHO1.0, we construct a hybrid model to quantify the coupled effect of sediments, oceanic and continental lithosphere, and anelastic upper mantle on OTL and OTD. Compared to PREM, this more realistic 3D model produces significantly larger vertical OTL displacement by up to 3.9, 2.6, and 0.1 mm for the M2, K1, and Mf OTL, respectively. Moreover, it shows a smaller vector difference of 0.1 mm and a smaller amplitude difference of 0.2 mm than PREM with OTL observations at 663 Global Navigation Satellite System stations, a confirmation of the cumulative effect due to these earth features. On the other hand, we find a resonant impact of wider extent and larger magnitude on OTD, especially for the M2 and K1 tides. Specifically, this impact is concentrated in the ranges 0–6 mm and 0–1.5 mm for M2 and K1, respectively, which is considerably larger than the impact on SAL (mostly in the ranges 0–2 mm and 0–1.0 mm, respectively). Since the effect on vertical displacement is at a similar level compared to the accuracy of modern data-constrained ocean tide models that require correction of the geocentric tide by loading induced vertical displacements, we regard its consideration to be potentially beneficial in OTD modeling

Accelerating Ice Loss From Peripheral Glaciers in North Greenland

In recent decades, Greenland's peripheral glaciers have experienced large‐scale mass loss, resulting in a substantial contribution to sea level rise. While their total area of Greenland ice cover is relatively small (4%), their mass loss is disproportionally large compared to the Greenland ice sheet. Satellite altimetry from Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat‐2 shows that mass loss from Greenland's peripheral glaciers increased from 27.2 ± 6.2 Gt/yr (February 2003–October 2009) to 42.3 ± 6.2 Gt/yr (October 2018–December 2021). These relatively small glaciers now constitute 11 ± 2% of Greenland's ice loss and contribute to global sea level rise. In the period October 2018–December 2021, mass loss increased by a factor of four for peripheral glaciers in North Greenland. While peripheral glacier mass loss is widespread, we also observe a complex regional pattern where increases in precipitation at high altitudes have partially counteracted increases in melt at low altitude

Greenland Mass Trends From Airborne and Satellite Altimetry During 2011–2020

We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product

Scaling full seismic waveform inversions

The main goal of this research study is to scale full seismic waveform inversions using the adjoint-state method to the data volumes that are nowadays available in seismology. Practical issues hinder the routine application of this, to a certain extent theoretically well understood, method. To a large part this comes down to outdated or flat out missing tools and ways to automate the highly iterative procedure in a reliable way. This thesis tackles these issues in three successive stages. It first introduces a modern and properly designed data processing framework sitting at the very core of all the consecutive developments. The ObsPy toolkit is a Python library providing a bridge for seismology into the scientific Python ecosystem and bestowing seismologists with effortless I/O and a powerful signal processing library, amongst other things. The following chapter deals with a framework designed to handle the specific data management and organization issues arising in full seismic waveform inversions, the Large-scale Seismic Inversion Framework. It has been created to orchestrate the various pieces of data accruing in the course of an iterative waveform inversion. Then, the Adaptable Seismic Data Format, a new, self-describing, and scalable data format for seismology is introduced along with the rationale why it is needed for full waveform inversions in particular and seismology in general. Finally, these developments are put into service to construct a novel full seismic waveform inversion model for elastic subsurface structure beneath the North American continent and the Northern Atlantic well into Europe. The spectral element method is used for the forward and adjoint simulations coupled with windowed time-frequency phase misfit measurements. Later iterations use 72 events, all happening after the USArray project has commenced, resulting in approximately 150`000 three components recordings that are inverted for. 20 L-BFGS iterations yield a model that can produce complete seismograms at a period range between 30 and 120 seconds while comparing favorably to observed data

Scaling full seismic waveform inversions

The main goal of this research study is to scale full seismic waveform inversions using the adjoint-state method to the data volumes that are nowadays available in seismology. Practical issues hinder the routine application of this, to a certain extent theoretically well understood, method. To a large part this comes down to outdated or flat out missing tools and ways to automate the highly iterative procedure in a reliable way. This thesis tackles these issues in three successive stages. It first introduces a modern and properly designed data processing framework sitting at the very core of all the consecutive developments. The ObsPy toolkit is a Python library providing a bridge for seismology into the scientific Python ecosystem and bestowing seismologists with effortless I/O and a powerful signal processing library, amongst other things. The following chapter deals with a framework designed to handle the specific data management and organization issues arising in full seismic waveform inversions, the Large-scale Seismic Inversion Framework. It has been created to orchestrate the various pieces of data accruing in the course of an iterative waveform inversion. Then, the Adaptable Seismic Data Format, a new, self-describing, and scalable data format for seismology is introduced along with the rationale why it is needed for full waveform inversions in particular and seismology in general. Finally, these developments are put into service to construct a novel full seismic waveform inversion model for elastic subsurface structure beneath the North American continent and the Northern Atlantic well into Europe. The spectral element method is used for the forward and adjoint simulations coupled with windowed time-frequency phase misfit measurements. Later iterations use 72 events, all happening after the USArray project has commenced, resulting in approximately 150`000 three components recordings that are inverted for. 20 L-BFGS iterations yield a model that can produce complete seismograms at a period range between 30 and 120 seconds while comparing favorably to observed data

Relating Streamflow Discharge to Surface Elastic Response Under Hydrologic Loading Using Single GPS Vertical Displacement and the Storage-Discharge Relationship at Local Watershed Scales

Uncertainties associated with climate change and increasing demands for water resources require better methods for estimating water availability at small to intermediate watershed scales (\u3c1500 km2). Temporal changes in watershed storage and transport across various watersheds in the western U.S. were investigated using the hydrologic loading signal from GPS vertical displacements as a proxy for changes in watershed total terrestrial storage. GPS vertical displacement and streamflow discharge relationships were analyzed at daily to monthly temporal resolution. Stream connected storage changes were inferred using discharge using a first-order dynamical system model. Storage inferred from discharge, GPS vertical displacement and storage inferred from a regional scale western U.S. GPS network array were compared. Analyzing the average behavior over the period of record (10+ years), we find that GPS vertical displacement is well correlated to discharge during periods of hydrograph recession resulting in R2 values ranging from (0.78 to 0.96) with 30-day smoothing. We show that local GPS measurements are in close agreement with regional GPS storage inferences. When GPS station array density is sparse, local GPS stations display better agreement with discharge inferred storage estimates and have the potential to provide higher spatial and temporal resolution relative to current published methods of inferring storage from regional GPS inversions. The GPS vertical displacement-discharge relationship provides an independent analysis of watershed function, insight into antecedent conditions, and strong correlations that may enhance predictive power when estimating water availability at local watershed scales most useful to hydrologist and water resources management