Figure 1 - uploaded by Kimberly DeGrandpre
Content may be subject to copyright.
6. Schematic of a satellite altimeter. This figure illustrates how the satellite is referenced to the ellipsoid and makes measurements of sea surface height (i.e., MSL), that are then also referenced to the geoid. The relationship between MSL, dynamic topography, the geoid, and ellipsoid are clearly illustrated here. Modified from AVISO, 2009.
Similar publications
Citations
... We conclude that our best-fit rheological model is only weakly sensitive to errors in the recent load model, and of course, a model with a constant scaling factor cannot fit the observed change in velocities. Sato et al. (2011) used the ICE-3G model with an Earth model appropriate to Southeast Alaska to assess the present-day influence of deglaciation of the Laurentide ice sheet on geodetic observables in Southeast Alaska and found that the effects were <1 mm/year in southeast Alaska; model predictions are larger in western Alaska and in NW Canada (DeGrandpre, 2015). Model predictions for the more recent ICE-5G and ICE-6G (each using the preferred global viscosity model for that ice model) are now available on W. R. Peltier's website (http://atmosp.physics.utoronto.ca/~peltier/data.php, ...
Global Positioning System observations in Southeast Alaska show evidence for interannual variations in uplift rates, which are consistent with an acceleration of mass loss rates across the region from the 1990s to 2012. We constructed an updated regional loading model based on updated twentieth century average deglaciation estimates. This model features a larger mass loss rate overall than the model used in previous studies. We adopted a viscosity model with an upper and lower mantle viscosity from VM5a but with a low viscosity asthenosphere as in previous regional studies. We varied the asthenospheric thickness, asthenospheric viscosity, and lithospheric thickness to find the Earth model that best fits the data, which has a lithospheric thickness of 55 km and an asthenosphere with thickness 230 km and viscosity 3 × 10¹⁹ Pa s. There is a strong tradeoff between the asthenosphere thickness and viscosity, and different estimates or assumptions about the asthenospheric thickness is one of the main causes of the varying viscosity estimates of previous studies. We find a higher viscosity than previous studies in part because we find that a thicker asthenosphere fits the data better than a thinner one and also because the higher twentieth century mass loss rate compared to earlier studies requires a higher viscosity to predict the same uplift rates. Vertical velocities predicted by the model drop rapidly with distance from the ice masses, while horizontal velocities are smaller but extend for a longer distance.
... Some of these measurements were the first or second occupation of the site, and such data will not be used in this study. All measurements are documented in DeGrandpre (2015). The data obtained during these surveys are stored in the UNAVCO data archive and were made publicly available as OPUS solutions through the National Geodetic Survey shared database. ...
... These large residuals ( Figure 5) may result from a real, local tectonic motion that is not explained by glacial rebound or they could be related to error in the measurement. In an effort to utilize the most accurate tectonic vertical velocity estimates the sites with large residuals are evaluated (DeGrandpre, 2015). This analysis attempts to identify the residuals that result from local tectonic or other factors, and thus likely represent the true ground motions, from velocities that are likely an artifact of measurement error, so that the GIA modeling predictions are more likely to be representative of the true ground motion. ...
Glacial isostatic adjustment (GIA), resulting from the Pleistocene loading of the Laurentide and Cordilleran ice sheets, is frequently associated with positive vertical velocities, or uplift. In Northern and Western Alaska, thousands of kilometers from the center of these ice sheets, vertical motion is primarily negative, or subsidence. Previously, no regional Earth structure model has been estimated for these areas using GIA modeling techniques, and the contribution of GIA processes to the observed subsidence signal has not been studied. We compare the vertical motion rates from 54 campaign and continuous GPS sites in Northern and Western Alaska to the predictions of the ICE‐5G and ICE‐6G GIA models, and to a suite of models that vary with four adjustable parameters defining the lithospheric thickness, asthenospheric thickness and viscosity, and upper mantle thickness and viscosity with the ICE‐3G loading model of Tushingham and Peltier (1991, https://doi.org/10.1029/90JB01583). The best overall fit with the ICE‐3G loading model and Earth model parameters were 120‐km lithosphere over a 100‐km asthenosphere with a viscosity of 2.5 × 10¹⁹ Pa/s, overlying a 450‐km‐thick upper mantle with a viscosity of 1.5 × 10²¹ Pa/s. These values are for a fixed lower mantle viscosity of 3 × 10²¹ Pa/s in a one‐dimensional Earth model that uses a linear Maxwell rheology. The GIA estimates are found to fit the GPS observations well and can be used to more accurately interpolate between measurement sites in a region where there is sparse spatial and temporal coverage of tectonic vertical velocities.
... Total velocity at each site was estimated using the velocity model and methods described in Section 3.1. Additionally, a linear least squares method (weighted by the uncertainty of each measurement) was used to obtain an average velocity and uncertainty for all of the continuous sites on Unalaska, this averaged velocity is referred to here as "weighted mean" (DeGrandpre, 2015). Three of the sites (MAPS, MREP, and MSWB) are located on the western flanks of Makushin volcano, and site AV09 has visibly noticeable noise, so site DUTC was also used separately as an estimate, to compare the potential input from Makushin volcano Each of these three sets of data were then used two different ways to calculate volcanic deformation, with tectonic estimates being removed from only the horizontal (east and north) components, or by removing the tectonic estimate from all three components (east, north, vertical). ...
Akutan is one of the most active volcanoes in the Aleutian island arc. Studies involving seismic, GPS, and InSAR data have observed activity and deformation on the island since 1996. In this study we inverted measurements of volcanic deformation, observed using three components of motions at 12 continuous GPS sites to define magma source parameters using Mogi point source, Okada dislocation, and Yang spheroid and ellipsoid models. In order to analyze the evolution of this magma source we split the GPS data into five consecutive time periods, and one period that incorporates all available data. These time periods were designed around two inflation events in 2008 and 2014, when a sudden and significant increase in vertical velocity was observed. Inversion of these time periods independently allowed us to create a magma volume time-series that is related to the physical migration of magma defined by the estimated source parameters. The best fit model parameters resulting from these inversions describes magma storage in the form of an oblate spheroid centered on the northeastern rim of the caldera of Akutan volcano, extending from a depth of 7km to 8km, with a length of ~3.5km, a strike of ~N165°E, and a dip of ~63° from the horizontal to the southwest. Our model results were compared with seismic studies and found to support previous interpretations of episodic inflation beneath Akutan volcano with complicated magma storage at intermediate depths. The inflation event observed in 2008 was estimated to be the result of an injection of magma of ~0.08km³ that was followed in 2014 by an additional increase in volume of ~0.06km³. No periods of deflation were observed in the GPS data after these events, and we believe the total volume of magma accumulated in this region, ~0.2km³, remains in a shallow storage system beneath Akutan Volcano.
Coastal hazards are of increasing concern to many of Alaska’s rural communities, yet quantitative assessments remain absent over much of the coast. To demonstrate how to fill this critical information gap, an erosion and flood analysis was conducted for Goodnews Bay using an assortment of datasets that are commonly available to Alaska coastal communities. Measurements made from orthorectified aerial imagery from 1957 to 2016 show the shoreline eroded 0 to 15.6 m at a rate that posed no immediate risk to current infrastructure. Storm surge flood risk was assessed using a combination of written accounts, photographs of storm impacts, GNSS measurements, hindcast weather models, and a digital surface model. Eight past storms caused minor to major flooding. Wave impact hour calculations showed that the record storm in 2011 doubled the typical annual wave impact hours. Areas at risk of erosion and flooding in Goodnews Bay were identified using publicly available datasets common to Alaska coastal communities; this work demonstrates that the data and tools exist to perform quantitative analyses of coastal hazards across Alaska.
We resurveyed preexisting campaign Global Positioning System (GPS) sites and estimated a highly precise GPS velocity field for the Alaska Peninsula. We use the TDEFNODE software to model the slip deficit distribution using the new GPS velocities. We find systematic misfits to the vertical velocities from the optimal model that fits the horizontal velocities well, which cannot be explained by altering the slip distribution, so we use only the horizontal velocities in the study. Locations of three boundaries that mark significant along-strike change in the locking distribution are identified. The Kodiak segment is strongly locked, the Semidi segment is intermediate, the Shumagin segment is weakly locked, and the Sanak segment is dominantly creeping. We suggest that a change in preexisting plate fabric orientation on the downgoing plate has an important control on the along-strike variation in the megathrust locking distribution and subduction seismicity.
