Large scale processing of seismic data in search of regional and global stress patterns

A composite compute program has been devised for a fast reduction of multistation seismic data in the period range 50–500 sec for mantle surface waves and 20–100 sec for body waves. The analysis aims at the reconstruction of the seismic source from the spectrum of its far radiation field and the correlation of its parameters with its depth, size and regional environment. The capability of the computational procedure has been demonstrated in two studies of WWNSS records: one includes a spectral analysis of surface waves from a shallow shock in the Kurile Islands; the other includes a spectral analysis of P waves from 9 shocks in the depth range 550–700 km at Fiji, Mariana, Java, Japan, Peru and Brazil. Other applications of the proposed data processing routine are foreseen; a tsunami warning system and focal depth determination from spectral modal ratios. It is believed that a persistent search for stress patterns, based on the processing of a sufficiently large sample of seismic events, is essential to any future program of earthquake prediction

A Hierarchical Spatio-Temporal Statistical Model Motivated by Glaciology

In this paper, we extend and analyze a Bayesian hierarchical spatio-temporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate random walk. For computational efficiency, linear algebra for bandwidth limited matrices is utilized, and first-order emulator inference allows for the fast emulation of a numerical partial differential equation (PDE) solver. A test scenario from a physical system motivated by glaciology is used to examine the speed and accuracy of the computational methods used, in addition to the viability of modeling assumptions. We conclude by discussing how the model and associated methodology can be applied in other physical contexts besides glaciology.Comment: Revision accepted for publication by the Journal of Agricultural, Biological, and Environmental Statistic

A numerical model for meltwater channel evolution in glaciers

Meltwater channels form an integral part of the hydrological system of a glacier. Better understanding of how meltwater channels develop and evolve is required to fully comprehend supraglacial and englacial meltwater drainage. Incision of supraglacial stream channels and subsequent roof closure by ice deformation has been proposed in recent literature as a possible englacial conduit formation process. Field evidence for supraglacial stream incision has been found in Svalbard and Nepal. In Iceland, where volcanic activity provides meltwater with temperatures above 0 °C, rapid enlargement of supraglacial channels has been observed. Supraglacial channels provide meltwater through englacial passages to the subglacial hydrological systems of big ice sheets, which in turn affects ice sheet motion and their contribution to eustatic sea level change. By coupling, for the first time, a numerical ice dynamic model to a hydraulic model which includes heat transfer, we investigate the evolution of meltwater channels and their incision behaviour. We present results for different, constant meltwater fluxes, different channel slopes, different meltwater temperatures, different melt rate distributions in the channel as well as temporal variations in meltwater flux. The key parameters governing incision rate and depth are channel slope, meltwater temperature loss to the ice and meltwater flux. Channel width and geometry are controlled by melt rate distribution along the channel wall. Calculated Nusselt numbers suggest that turbulent mixing is the main heat transfer mechanism in the meltwater channels studied

Phosphorus (P) availability and P transformation rates in Amazonian Dark Earth determined by 33P isotopic dilution.

Amazonian Dark Earth (ADE) often shows increased soil fertility compared to surrounding soils, likely linked to its increased concentration of soil organic matter and macronutrients such as phosphorus (P). For P, there is little knowledge whether the higher P availability is a result of increased biological processes (such as organic P mineralization) or physico-chemical processes (such as sorption/desorption), or both

Spring warming in Yukon mountains is not amplified by the snow albedo feedback

Decreasing spring snow cover may amplify Arctic warming through the snow albedo feedback. To examine the impact of snowmelt on increasing temperature we used a 5,000 m elevation gradient in Yukon, Canada, extending from valley-bottom conifer forests, through middle elevation tundra, to high elevation icefields, to compare validated downscaled reanalysis air temperature patterns across elevational bands characterized by different patterns of spring snowmelt. From 2000 to 2014 we observed surface warming of 0.01 °C/a·1,000 m in May (0.14 °C/a at 1,000 m to 0.19 °C/a at 5,000 m), and uniform cooling of 0.09 °C/a in June at all elevations. May temperature trends across elevationally dependent land cover types were highly correlated with each other despite large variations in albedo and snow cover trends. Furthermore, a clear dependency of infrared skin temperature on snow cover mediated albedo decline was observed in tundra, but this was insufficient to influence average diurnal air temperature. We observed negative June temperature trends which we attribute to increasing daytime cloud cover because albedo and snow cover trends were unchanging. We conclude that 8-day and monthly averaged Spring air temperature trends are responding to a synoptic external forcing that is much stronger than the snow albedo feedback in sub-Arctic mountains

A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions

Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error-correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.The Icelandic Research Fund (RANNIS) is thanked for funding this research.Peer Reviewe

Winter mass balance of Drangajökull ice cap (NW Iceland) derived from satellite sub-meter stereo images

Sub-meter resolution, stereoscopic satellite images allow for the generation of accurate and high-resolution digital elevation models (DEMs) over glaciers and ice caps. Here, repeated stereo images of Drangajökull ice cap (NW Iceland) from Pléiades and WorldView2 (WV2) are combined with in situ estimates of snow density and densification of firn and fresh snow to provide the first estimates of the glacier-wide geodetic winter mass balance obtained from satellite imagery. Statistics in snow- and ice-free areas reveal similar vertical relative accuracy (<  0.5 m) with and without ground control points (GCPs), demonstrating the capability for measuring seasonal snow accumulation. The calculated winter (14 October 2014 to 22 May 2015) mass balance of Drangajökull was 3.33 ± 0.23 m w.e. (meter water equivalent), with ∼ 60 % of the accumulation occurring by February, which is in good agreement with nearby ground observations. On average, the repeated DEMs yield 22 % less elevation change than the length of eight winter snow cores due to (1) the time difference between in situ and satellite observations, (2) firn densification and (3) elevation changes due to ice dynamics. The contributions of these three factors were of similar magnitude. This study demonstrates that seasonal geodetic mass balance can, in many areas, be estimated from sub-meter resolution satellite stereo images.This study was funded by the University of Iceland (UI) Research Fund. Pleiades images were acquired at research price thanks to the CNES ISIS program (http://www.isis-cnes.fr). The WV2 DEM was obtained through the ArcticDEM project. This work is a contribution to the Rannis grant of excellence project, ANATILS. Collaboration and travels between IES and LEGOS were funded by the Jules Verne research fund and the TOSCA program from the French Space Agency, CNES. This study used the recent lidar mapping of the glaciers in Iceland that was funded by the Icelandic Research Fund, the Landsvirkjun research fund, the Icelandic Road Administration, the Reykjavik Energy Environmental and Energy Research Fund, the Klima-og Luftgruppen (KoL) research fund of the Nordic Council of Ministers, the Vatnajokull National Park, the organization Friends of Vatnajokull, the National Land Survey of Iceland, the Icelandic Meteorological Office and the UI research fund. The ground-based mass balance measurements on Drangajokull have been jointly funded by Orkubu Vestfjarda (Westfjord Power Company), the National Energy Authority (2004-2009) and the Icelandic Meteorological Office (2009-2015).Peer Reviewe

A Hierarchical Spatiotemporal Statistical Model Motivated by Glaciology

This is a post-peer-review, pre-copyedit version of an article published in Journal of Agricultural, Biological and Environmental Statistics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s13253-019-00367-1In this paper, we extend and analyze a Bayesian hierarchical spatiotemporal model for physical systems. A novelty is to model the discrepancy between the output of a computer simulator for a physical process and the actual process values with a multivariate random walk. For computational efficiency, linear algebra for bandwidth limited matrices is utilized, and first-order emulator inference allows for the fast emulation of a numerical partial differential equation (PDE) solver. A test scenario from a physical system motivated by glaciology is used to examine the speed and accuracy of the computational methods used, in addition to the viability of modeling assumptions. We conclude by discussing how the model and associated methodology can be applied in other physical contexts besides glaciology.Icelandic Centre for Research (152457).Peer reviewe

Non-surface mass balance of glaciers in Iceland

Publisher's version (útgefin grein)Non-surface mass balance is non-negligible for glaciers in Iceland. Several Icelandic glaciers are in the neo-volcanic zone where a combination of geothermal activity, volcanic eruptions and geothermal heat flux much higher than the global average lead to basal melting close to 150 mm w.e. a−1 for the Mýrdalsjökull ice cap and 75 mm w.e. a−1 for the largest ice cap, Vatnajökull. Energy dissipation in the flow of water and ice is also rather large for the high-precipitation, temperate glaciers of Iceland resulting in internal and basal melting of 20–150 mm w.e. a−1. The total non-surface melting of glaciers in Iceland in 1995–2019 was 45–375 mm w.e. a−1 on average for the main ice caps, and was largest for Mýrdalsjökull, the south side of Vatnajökull and Eyjafjallajökull. Geothermal melting, volcanic eruptions and the energy dissipation in the flow of water and ice, as well as calving, all contribute, and thus these components should be considered in mass-balance studies. For comparison, the average mass balance of glaciers in Iceland since 1995 is −500 to −1500 mm w.e. a−1. The non-surface mass balance corresponds to a total runoff contribution of 2.1 km3 a−1 of water from Iceland.Financial support for lidar mapping of glaciers in Iceland in 2008–2012 was provided by the Icelandic Research Fund (163391-052), the Landsvirkjun (National Power Company of Iceland) Research Fund, the Icelandic Road Administration, the Reykjavík Energy Environmental and Energy Research Fund, the National Land Survey of Iceland, the Klima- og Luftgruppen (KoL) research fund of the Nordic Council of Ministers, and the Vatnajökull National Park. The acquisition of the Hofsjökull 2013 DEM was funded by AlpS GmbH and the University of Innsbruck. The acquisition of the Langjökull 2013 DEM was funded by NERC grant IG 13/12 and the DEM was provided by Ian Willis at the Scott Polar Research Institute. The work on estimating geothermal and volcanic power is based on funding from many sources, including the Research Fund of the University of Iceland, ISAVIA (the Icelandic Aviation Service), the Icelandic Road Administration and Landsvirkjun; logistical support has been provided by the Iceland Glaciological Society.Peer Reviewe