Detecting a salt dome overhang with magnetotellurics: 3D inversion methodology and synthetic model studies

Detecting a salt dome overhang is known to be problematic by seismic methods alone. We used magnetotellurics (MT) as a complementary method to seismics to investigate the detectability of a salt dome overhang. A comparison of MT responses for 3D synthetic salt models with and without overhang shows that MT is very sensitive to shallow salt structures and suggests that it should be possible to detect an overhang. To further investigate the resolution capability of MT for a salt dome overhang, we performed a 3D MT inversion study and investigated the impact of model parametrization and regularization. We showed that using the logarithms of the conductivities as model parameters is crucial for inverting data from resistive salt structures because, in this case, commonly used Tikhonov-type stabilizers work more equally for smoothing the resistive and conductive structures. The use of a logarithmic parametrization also accelerated the convergence and produced better inversion results. When the Laplace operator was used as a regularization functional, we still observed that the inversion algorithm allows spatial resistivity gradients. These spatial gradients are reduced if a regularization based on first derivatives in contrast to the Laplace operator is introduced. To demonstrate the favorable performance when logarithmic parametrization and gradient-based regularization are employed, we first inverted a data set simulated for a simple model of two adjacent blocks. Subsequently, we applied the code to a more realistic salt dome overhang detectability study. The results from the detectability study are encouraging and suggest that 3D MT inversion can be applied to decide whether the overhang is present in the shallow salt structure even in the case when only profile data are available. However, to resolve the overhang, a dense MT site coverage above the flanks of the salt dome is required

Structures of the (Imidazole)nH+ ... Ar (n=1,2,3) complexes determined from IR spectroscopy and quantum chemical calculations

Here, we present new cryogenic infrared spectra of the (Imidazole)$_{n}H^+$ (n=1,2,3) ions. The data was obtained using helium tagging infrared predissociation spectroscopy. The new results were compared with the data obtained by Gerardi et al. (Chem. Phys. Lett. 501:172–178, 2011) using the same technique but with argon as a tag. Comparison of the two experiments, assisted by theoretical calculations, allowed us to evaluate the preferable attachment positions of argon to the (Imidazole)$_{n}H^+$ frame. Argon attaches to nitrogen-bonded hydrogen in the case of the (Imidazole)$H^+$ ion, while in (Imidazole)$_{2}H^+$ and (Imidazole)$_{3}H^+$ the preferred docking sites for the argon are in the center of the complex. This conclusion is supported by analyzing the spectral features attributed to the N–H stretching vibrations. Symmetry adapted perturbation theory (SAPT) analysis of the non-covalent forces between argon and the (Imidazole)$_{n}H^+$ (n=1,2,3) frame revealed that this switch of docking preference with increasing complex size is caused by an interplay between induction and dispersion interactions

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels

ACS imaging of 25 galaxies in nearby groups and in the field

We present HST/ACS images and color-magnitude diagrams for 25 nearby galaxies with radial velocities V_LG < 500 km/s. Distances are determined based on the luminosities of stars at the tip of the red giant branch that range from 2 Mpc to 12 Mpc. Two of the galaxies, NGC 4163 and IC 4662, are found to be the nearest known representatives of blue compact dwarf (BCD) objects. Using high-quality data on distances and radial velocities of 110 nearby field galaxies, we derive their mean Hubble ratio to be 68 km/(s Mpc) with standard deviation of 15 km/(s Mpc). Peculiar velocities of most of the galaxies, V_pec = V_LG - 68 D, follow a Gaussian distribution with sigma_v = 63 km/s, but with a tail towards high negative values. Our data displays the known correlation between peculiar velocity and galaxy elevation above the Local Supercluster plane. The small observed fraction of galaxies with high peculiar velocities, V_pec < -500 km/s, may be understood as objects associated with nearby groups (Coma I, Eridanus) outside the Local volume.Comment: 17 pages, 8 figures, accepted to A

Phenological shifts of abiotic events, producers and consumers across a continent

Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes. The authors use systematic monitoring across the former USSR to investigate phenological changes across taxa. The long-term mean temperature of a site emerged as a strong predictor of phenological change, with further imprints of trophic level, event timing, site, year and biotic interactions.Peer reviewe

Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology

We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change.Peer reviewe