On the Occurence of Extreme Events in Long-term Correlated and Multifractal Data Sets

We review recent studies of the statistics of return intervals (i) in long-term correlated monofractal records and (ii) in multifractal records in the absence (or presence) of linear long-term correlations. We show that for the monofractal records which are long-term power-law correlated with exponent γ, the distribution density of the return intervals follows a stretched exponential with the same exponent γ and the return intervals are long-term correlated, again with the same exponent γ. For the multifractal record, significant differences in scaling behavior both in the distribuiton and correlation behavior of return intervals between large events of different magnitudes are demonstrated. In the absence of linear long-term correlations, the nonlinear correlations contribute strongly to the statistics of the return intervals such that the return intervals become long-term correlated even though the original data are linearly uncorrelated (i.e., the autocorrelation function vanishes). The distribution density of the return intervals is mainly described by a power law.     

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs¹³⁷ activity given the measurements taken in the region of Briansk following the Chernobyl accident.     

Magnetosheath Structure in an Anisotropic Plasma Model

Variations of the magnetic field and plasma parameters across the Earth's magnetosheath are calculated for an anisotropic plasma in the Chew-Goldberger-Low approximation. Additionally, proton pitch-angle diffusion is taken into account as the energy transfer mechanism between the direction perpendicular and parallel to the magnetic field. We discuss the main characteristics of the magnetic barrier for different temperature relaxation times and their dependence on the interplanetary magnetic field orientation. This revised version was published online in July 2006 with corrections to the Cover Date.     

Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

This paper explores the use of adaptive support vector machines, random forests and AdaBoost for landslide susceptibility mapping in three separated regions of Canton Vaud, Switzerland, based on a set of geological, hydrological and morphological features. The feature selection properties of the three algorithms are studied to analyze the relevance of features in controlling the spatial distribution of landslides. The elimination of irrelevant features gives simpler, lower dimensional models while keeping the classification performance high. An object-based sampling procedure is considered to reduce the spatial autocorrelation of data and to estimate more reliably generalization skills when applying the model to predict the occurrence of new unknown landslides. The accuracy of the models, the relevance of features and the quality of landslide susceptibility maps were found to be high in the regions characterized by shallow landslides and low in the ones with deep-seated landslides. Despite providing similar skill, random forests and AdaBoost were found to be more efficient in performing feature selection than adaptive support vector machines. The results of this study reveal the strengths of the classification algorithms, but evidence: (1) the need for relying on more than one method for the identification of relevant variables; (2) the weakness of the adaptive scaling algorithm when used with landslide data; and (3) the lack of additional features which characterize the spatial distribution of deep-seated landslides.     

Erosion processes in karst landscapes of the Russian plain northern taiga,based on digital elevation modeling

This paper considers the problems of the potential development of erosion processes in the natural landscapes of northern taiga in the Russian plain. It is considered that in forest ecosystems, erosion processes are slow and are weakly reflected in the terrain. However, the situation changes radically if the vegetation cover integrity is violated, which is inevitable with the modern methods of developing northern territories. Furthermore, global changes in average annual temperatures and the occurrence of karst processes may be the reason behind the development of erosion processes. The authors suggest a method for determining territories with a varying occurrence probability of erosional processes, based on digital elevation modelling. The territory of the Pinezhsky Nature Reserve (Arkhangelsk region) was chosen as the test plot. Direct field studies were previously used to detect exogenous geological processes in this territory. The authors were the first to suggest digital elevation modelling as a method that allows determining the potential danger of erosion in karst landscapes of the northern taiga. The geomorphometric studies resulted in the determination of areas with the greatest and lowest occurrence probability of erosion processes in the Pinezhsky Nature Reserve. It was established that the most significant erosion type was linear erosion, represented by incised river valleys and karst ravines. Sheet erosion is less significant and occurs as sinkholes, local declines, and chasms over the valleys of subterranean rivers.     

Response of the Arctic benthic community to excessive amounts of nontoxic organic matter

The paper presents a study of the effect of excessive amounts of organic matter (OM) on Arctic benthos. The source of nontoxic OM is from the mariculture of blue mussels in the White Sea (north-western area of Russia). The use of multivariate analysis procedures made it evident that in the Arctic conditions benthos is incapable of annual utilization of more than 1.5 kg dry OM m⁻² from an external source. Mineralization of OM accumulated in sediments will deteriorate the oxygen regime. The critical concentration of OM in sediments is 9–10%. An excess over this threshold leads to the formation of oxygen-free zones at the bottom, and degradation of benthos community.     

Increasing Accuracy of the GLONASS Time Service

GLONASS time is the reference for all time scales aboard GLONASS satellites. It is derived from the Central Synchronizer (CS), which consists of an ensemble of hydrogen clocks (HC) having a instability of (1...3) × 10⁻¹⁴ (averaging interval 24 hours). The CS time is compared to UTC(SU) in a differential mode using a TV channel of the Technical TV Center. Presently the error of GLONASS time is within 20...150 ns; however, there is a potential to decrease this value to 15 to 50 ns (3σ). The paper discusses the possible benefits of mutual phase comparisons between the HCs to determine the grouped frequency standard. These comparisons are averaged daily. The approach yields an accuracy of better than 10 ns (3σ) for CS, bringing the expected GLONASS time accuracy to the level of 15 to 50 ns (3σ). ? 2001 John Wiley & Sons, Inc.     

Magnetic properties of the LL5 ordinary chondrite Chelyabinsk (fall of February 15, 2013)

Here we characterize the magnetic properties of the Chelyabinsk chondrite (LL5, S4, W0) and constrain the composition, concentration, grain size distribution, and mineral fabric of the meteorite's magnetic mineral assemblage. Data were collected from 10 to 1073 K and include measurements of low‐field magnetic susceptibility (χ₀), the anisotropy of χ₀, hysteresis loops, first‐order reversal curves, Mössbauer spectroscopy, and X‐ray microtomography. The REM and REM′ paleointensity protocols suggest that the only magnetizations recorded by the chondrite are components of the Earth's magnetic field acquired during entry into our planet's atmosphere. The Chelyabinsk chondrite consists of light and dark lithologies. Fragments of the light lithology show logχ₀ = 4.57 ± 0.09 (s.d.) (n = 135), while the dark lithology shows 4.65 ± 0.09 (n = 39) (where χ₀ is in 10^?9 m³ kg^?1). Thus, Chelyabinsk is three times more magnetic than the average LL5 fall, but is similar to a subgroup of metal‐rich LL5 chondrites (Paragould, Aldsworth, Bawku, Richmond) and L/LL5 chondrites (Glanerbrug, Knyahinya). The meteorite's room‐temperature magnetization is dominated by multidomain FeNi alloys taenite and kamacite (no tetrataenite is present). However, below approximately 75 K remanence is dominated by chromite. The metal contents of the light and dark lithologies are 3.7 and 4.1 wt%, respectively, and are based on values of saturation magnetization.     

Jitter radiation images, spectra and light curves from a relativistic spherical blastwave

We consider radiation emitted by the jitter mechanism in a Blandford–McKee self-similar blastwave. We assume the magnetic field configuration throughout the whole blastwave meets the condition for the emission of jitter radiation and we compute the ensuing images, light curves and spectra. The calculations are performed for both a uniform and a wind environment. We compare our jitter results to synchrotron results. We show that jitter radiation produces slightly different spectra than synchrotron, in particular between the self-absorption and the peak frequency, where the jitter spectrum is flat, while the synchrotron spectrum grows as  ν^1/3  . The spectral difference is reflected in the early decay slope of the light curves. We conclude that jitter and synchrotron afterglows can be distinguished from each other with good quality observations. However, it is unlikely that the difference can explain the peculiar behaviour of several recent observations, such as flat X-ray slopes and uncorrelated optical and X-ray behaviour.     

Analyzing ice dynamics using Sentinel-1 data at the Solheimajoküll Glacier,Iceland

ABSTRACT

The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There have been two regional temperature trends in the study period: cooling (1973–1979) and warming (1980–2018). Our results indicate a time lag of about 20 years between the onset of glacier retreat (?53 m/year since 2000) and the inception of the warming period. Seasonally, the velocity time series suggest acceleration during the summer melt season since 2016, whereas glacier velocities during accumulation months were constant. The highest velocities were observed at high elevations where the ice-surface slope is the steepest. We tested several scenarios to assess the hydrological time response to glacier accelerations, with the highest correlations being found between one and 30 days after the velocity estimates. Monthly correlation analyses indicated inter-annual and intra-annual variability in the glacier dynamics. Additionally, we investigate the linkage between glacier velocities and meltwater outflow parameters as they provide useful information about internal processes in the glacier. Velocity estimates positively correlate with water level and negatively correlate with water conductivity between April and August. There is also a disruption in the correlation trend between water conductivity and ice velocity in June, potentially due to a seasonal release of geothermal water.