Addressing the need for convergence of different sources of knowledge to deal with complex issues such as global change, this paper presents the results of collaboration between artists and scientists to study social-ecological-technological systems (SETS). We focus on informal roads as an example of SETS. In the absence of public roads local, mostly indigenous communities and others use these forestry roads, seismic line clearings and oil and gas service roads for mobility in Siberian taiga affected by extractive industry. In 2020, with COVID-19, we had to increase our emphasis on virtual forms of data gathering, interpretation, and representations of the results. Presented in this paper forms of transmedia storytelling are designed to allow audience and users as well as the local and indigenous communities to get familiar with the research results, give feedback, and provide their own perspectives, interrelations and interdependencies between different SETS components.

Through time rivers move, create erosion, deposit sediments and, consequently, change their shape, however, these movements are different for each river, since each one has specific characteristics and dynamics. While small movements occur every day, it takes us months, years, decades, or even centuries to notice those changes. As rivers are the fundamental sources of life in many cities, towns, and communities, and even determine geopolitical limits, it is more important than ever to understand the dynamics that involve them. Dancing Rivers is a visualization platform that characterizes river patterns and positions the importance of scientific studies of the Amazonian rivers. It includes and displays information about the research, analysis and methodology developed to characterize planimetric morphodynamics (spatial analysis with remote sensing) and altimetry features (description of river bedforms and sediment transport) of major Amazonian rivers: Marañon, Huallaga, Ucayali and the Amazon, with the purpose of disseminating and getting a better understanding of their physical dynamics, allowing stakeholders and researchers to make wise decisions involving the water resource and territorial planning.

The Global Multi-Resolution Topography (GMRT) Synthesis is an elevation model that includes curated deep-water multibeam bathymetry data at ~100 m resolution covering more than 9% of the ocean. GMRT is built with a scalable tiled raster architecture that efficiently stores and presents high-resolution elevation data nested within low resolution data. A set of tools are available for users to access the compilation through simple user interfaces (e.g. GMRT MapTool) and web services, while also providing full attribution and access to source swath files. The availability of raw/unprocessed multibeam sonar data in the National Centers for Environmental Information (NCEI) archive has increased dramatically over the last decade, but transforming these data into high-quality integrated products suitable for use by scientists and the public alike requires significant effort. The GMRT Team has built workflows and tools for data preparation and review that are optimized for cleaning and integrating sparse globally distributed multibeam data, enabling the addition of ~60-80 research cruises per year. Once raw swath data files are cleaned and corrected, they are gridded/tiled with the GMRT Tiling tools so they can be reviewed and quality controlled in the context of other data in the GMRT Synthesis. Working with processed swath files generated by the community, we have observed that this process frequently reveals issues that are overlooked during data processing. In order to accelerate the rate of data integration and leverage the data processing efforts of the community, GMRT Tiling tools are being adapted for distributed use. Ocean Exploration Trust is an initial partner in this effort, and all processed swath files from the 2017-2019 Nautilus field seasons were prepared with GMRT Tiling tools and reviewed by the OET team. This revealed problems in processed swath data files from several cruises that were addressed prior to submission to NCEI, thereby improving the quality of data in the archive. We are now working to include the GMRT Tiling tools into at-sea standard operating procedures of the Nautilus as a testbed for broader community distribution, to ensure consistent quality of processed multibeam data, and to accelerate the production of high-quality integrated data products including GMRT and Seabed 2030.

Subduction zones showcase the multiplicity of earthquakes—interplate, intraplate and intraslab—with shallow, intermediate, or deep focus, associated with different energy release patterns and frequency contents. An understanding of the duration and frequencies associated with various pulses of energy is useful for damage assessment. Empirical Mode Decomposition (EMD) of strong-motion records and the application of Hilbert transform have been suggested to overcome the limitations of the Fourier spectral analysis in dealing with highly non-linear strong-motion records (Huang et al., 1998, Zhang et al., 2003). Following the same approach, we have been trying various methods of analysis using the KiK-net strong-motion records to explore the efficacy of these techniques in representing the source of the rupture, in terms of energy release and frequency distribution. Our previous studies used EMD and time-frequency analysis tools such as spectrogram, scalogram, and Hilbert spectrum, using Intrinsic Mode Functions (IMFs) of the original signals as inputs. Nishant (2019) made random picks of IMFs to represent sources by correlating the sum of the selected IMFs with the original signal but found that the results were station dependent. We selected IMFs based on their frequency content (0.1 to 3 Hz) and used their linear combinations to develop the Energy Release Functions (ERF) for individual earthquakes (Mache et al., 2019). They reported that the ability to capture the signature of the original signal using the IMFs varied between earthquakes and stations. Next, we selected stations based on the direction of rupture inferred from teleseismic waveform models. The use of appropriate combinations of individual IMFs, chosen based on the direction of slip, resulted in ERFs whose shapes compared better with the Moment Rate Functions (MRFs) obtained from the teleseismic models. To further explore the station dependence on the resolution of ERFs viz-a-viz the MRFs, we used the instrumental seismic intensity distribution maps (JMA 1996, Shabestari and Yamazaki 2001) to select the stations. We analyzed five earthquakes; two interplate (Mw 7.2 2005 Miyagi, and Mw 6.9 2008/07/19), two intraplate (Mw 7.0 2003 Sendai, and Mw 7.2 2012 Kamaishi) and one intraslab (Mw 7.1 2011 Miyagi), following the above methodologies. This abstract presents the initial results of our study, which to our knowledge, is the first of its kind and holds significant potential in understanding the spatial and temporal patterns of energy release and their associated frequencies. [cont.]

2019 was both the hottest and driest year on record for Australia, leading to large forest fires in the southeast from November 2019 to January 2020. However, in early 2020, the fires and hot-dry conditions dissipated with above average rainfall and below average temperatures along Australia’s southeast coast. In this study, we utilize space-based measurements of trace gases (TROPOMI XCO, OCO-2 XCO2) and vegetation function (OCO-2 SIF, MODIS NDVI) to quantify the carbon cycle anomalies resulting from drought and fire in southeast Australia during the 2019/2020 growing season. During the austral spring, we find anomalous reductions in primary productivity and large biomass burning emissions in excess of bottom-up estimates from GFAS. This is then followed by a remarkable recovery and greening during early 2020, coincident with cooler and wetter conditions. We will further discuss different behaviors of recovery over fire-devasted and non-fire regions. This study showcases the capability of combining observations from multiple satellites to monitor the carbon and ecosystem anomalies resulting from extreme events. Finally, we will discuss the remaining challenges in monitoring the carbon cycle from space.

The microstructures, textures and seismic anisotropy of a foliated blueschist-eclogite rock from Naga Hills Ophiolite (NHO) complex, North-East India has been investigated to understand the fabric relationship and deformation mechanism prevailed during the metamorphism and subduction of the Neo-Tethys oceanic crust. The Naga Hills blueschist-eclogite rock essentially contains omphacite, phengite, glaucophane, and garnet, representing an oceanic crust that experienced P-T stability field of blueschist and further eclogite metamorphism before it resurfaced accreting in the Naga Hills. Omphacite and glaucophane show weak shape preferred orientation (SPO), while phengite displays a strong SPO. The Crystallographic Preferred Orientations (CPO) of omphacite is characterized by the [001]-axes gridles within the foliation, and the (010)-poles concentrated sub-perpendicular to the foliation. For glaucophanes, the [001] axis aligns parallel to lineation and the [100] axis and (110) pole plunge perpendicular to foliation. These CPOs correspond to SL-type fabrics, related to a deformation geometry within the plain strain field and they developed from plastic deformation through dislocation creep. The seismic anisotropies of the individual minerals, blueschist-eclogite domains and their contributions in the bulk rock anisotropy has been discussed. The calculated seismic anisotropies (AVP and AVS) of bulk rock are 12.8% and 8.1%, respectively. This strong seismic anisotropy is due to the presence of phengite and glaucophane and can contribute to the observed seismic anisotropy in the subduction zone. The average low P-wave velocity of whole rock from NHO compared to blueschist is probably due to the low P-wave velocity of phengite (avg VP: 6.2 kms-1). Therefore, the low seismic velocity in the upper layer and the strong seismic anisotropy of the subducting oceanic crust can be attributed to the presence of glaucophane and phengite.

Although air pollution is largely due to anthropogenic emission, the observed pollution levels in a city are confounded by meteorological conditions and regional transportation of pollutants. However, effective air quality management requires measures for local emissions of the city. With a data selection algorithm, we choose calm episodes after strong cleaning processes to measure the growth of three air pollutants (PM2.5, NO2 and SO2) before the arrival of transported pollution. Panel data regression models are used to analyze the episode data from the quasi-experiments to quantify the local emission in three North China cities from March 2013 to February 2019. The study reveals a significant reduction in the average hourly growth rates for PM2.5 and SO2 in 2017-2018 as compared to the levels in 2013 in almost all seasons and cities. However, the local emission with respect to NO2 was little changed for almost all seasons and cities. The study also finds the winter growth rates of PM2.5 in Beijing were comparable to those in the heavy industrialized Tangshan and Baoding, even the PM2.5 hourly growth rates for winter 2018 in Beijing were higher than those in Tangshan and Baoding, revealing Beijing’s substantial emission.

Recently a new approach to change point analysis was presented in the statistical literature. This approach is based on the construction of confidence sets. One way to apply it is to take existing homogeneity tests as a basis. In this study Pettitt and CUSUM based homogeneity tests are used to derive distribution-free change point analysis methods. These are applied to a large number of synthetic data series, and the results are analyzed. The results are compared to results of the application of the classical Pettitt and CUSUM methods, and with a Bayesian approach. It is shown that the new methods perform as least as well as the classical methods and the Bayesian method. Unlike the classical methods, the Bayesian and confidence set based methods provide information on the uncertainty of the change point location. The methods are tested on normally distributed synthetic time series and on synthetic time series with a type II Generalized Extreme Value distribution.

Volcanism has played a major role in modifying the Martian surface. The Tharsis Volcanic Province dominates the western hemisphere of the planet with numerous effusive volcanic constructs and deposits. Here, we present the results of an in-depth study aimed at characterizing and modelling the emplacement conditions of 40 lava flows in the Tharsis Volcanic Province. These lava flows display a range of lengths (~15 – 314 km), widths (~0.5 – 29 m), and thicknesses (~11 – 91 km). The volumes and flow masses range from ~1 – 430 km3 and ~1011 – 1014 kg, respectively. Using three different models, we calculated a range of eruption rates (0.2 – 3.5 x 104 m3/s), viscosities (104 – 107 Pa s), yield strengths (800 – 104 Pa), and emplacement times (14 hours – 22 years). While the flow lengths and volumes are typically larger than terrestrial lava flows by an order of magnitude, rheologies and eruption rates are similar based on our findings. Emplacement times suggest that eruptions were active for long periods of time, which implies the presence and persistence of open subsurface pathways. Differences in flow morphology and emplacement conditions across localities within Tharsis highlight different pathways and volumes of available material between the central volcanoes and the plains. The scale of the eruptions suggests there could have been eruption-driven local, regional, and perhaps, global impacts on the Martian climate. The relatively recent age of the eruptions implies that Mars has retained the capability of producing significant localized volcanism.

This first paper of the two-part series focuses on demonstrating the predictability of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01 degree (∼ 1-km) and 0.25 degree (∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01 degree estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest predictability skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations.

The combination of X-ray imaging and CT image‒based computational fluid dynamics (CFD) simulation allows study of flow in fractured porous media. In this study, X-ray imaging was employed to unveil the morphological and aperture alterations of artificial fractures in wellbore cement cores that were exposed to CO2-saturated brine under geologic carbon sequestration (GCS) conditions. Direct pore-scale modelling of fluid flow through 3D fractures reconstructed from CT images was carried out to reveal velocity distribution in the fracture and for estimation of local and average permeability of the fracture. Varying-radius pipe representations of the fractures were established using the optimal characteristic radius formulation that was determined from the relation of flow cross-section shape and conductivity based on direct pore-scale modelling. Varying-radius pipeline modelling of fluid flow through simplified fractures was also implemented and the local and average permeability results based on varying-radius pipeline modelling were compared against those based on direct pore-scale modelling. The fracture after CO2 exposure in the reactive diffusion process was covered by substantial precipitated calcite, and the permeability of the fracture decreased from 4.15×10-8 m2 to 2.96×10-8 m2. In contrast, the fracture after CO2 exposure in the reactive flow process underwent significant dissolution, a large number of tensile micro-fractures were formed at the surface of the fracture, and the permeability of the fracture increased from 3.91×10-8 m2 to 4.23×10-8 m2. The relative error of the average fracture permeability obtained from direct pore-scale modelling (-7.33%‒4.05%) was comparable with that obtained from varying-radius pipeline modelling (-7.77%‒10.64%).

The Tanlu fault is the largest strike-slip fault in eastern China. It has experienced multiple episodes of tectonic activity suggested mostly by near-surface geological analysis. However, little evidence comes from deformation at different depths in the crust. This study presents a three-dimensional (3-D) high-resolution upper crustal azimuthally anisotropic model of shear-wave velocity beneath the Chao Lake segment of the Tanlu fault zone (TFZ-CL) using surface wave dispersion data (0.5–12 s) extracted from ambient seismic noise. The results show that fast directions of azimuthal anisotropy are consistent with local geological units, hence interpreted as shape-preferred orientations (SPOs). A special depth-dependent anisotropic pattern is revealed beneath eastern Chao Lake, where the fast directions vary from nearly NE-SW at shallow depths, to NNE-SSW or nearly N-S at intermediate depths and back to NE-SW at great depths. The anisotropic model is combined with other previously published datasets to construct a deformation model at multiple depths. The deformation model suggests that lithospheric deformation is decoupled from the asthenosphere beneath the TFZ-CL, implying that the deep tectonic setting could have changed dramatically since the Mesozoic. By connecting the new anisotropic model and the local tectonic episodes, we further propose a four-stage tectonic evolution model of the TFZ-CL. The model can well interpret the generation of anisotropies, especially the specific anisotropic pattern beneath eastern Chao Lake, providing new evidence of deformation at great depths to support the multistage tectonic episodes along the Tanlu fault.

High-pressure fluid injections cause transient pore pressure changes over large distances, which may induce seismicity. The zone of influence for such an injection was studied at high spatial resolutions in six decameter-scaled fluid injection experiments in crystalline rock. Pore pressure time series revealed two distinct responses based on the lag time and magnitude of pressure change, namely, a near- and far-field response. The near-field response is due to pressure diffusion. In the far-field, the fast response time and decay of pressure changes are produced by effective stress changes in the anisotropic stress field. Our experiments prove for the first time that fracture fluid pressure perturbations around the injection point are not limited to the near-field and can extend beyond the pressurized zone.

This paper will report on the effects of an extreme space weather event. On January 20th, 2005, a balloon-borne experiment intended to measure relativistic electron precipitation and its effects was aloft over Antarctica (~32 km; near 70º S, 345º W geographic) throughout the duration of the solar energetic particle (SEP) event. The balloon carried an x-ray scintillation counter, dc electric field, and scalar electrical conductivity sensors. Intense energetic proton precipitation and large increases in the energetic proton population of the outer radiation belts were observed by a global array of observatories and spacecraft. The stratospheric conductivity increased by nearly a factor of 20 above ambient at the time of the SEP event onset and returned to within a factor of two above normal levels within 17 hours. The electric field decreased to near zero following the increase in particle flux at SEP onset. Combined with an atmospheric electric field mapping model, these data are consistent with a shorting out of the global electric circuit and point toward substantial ionospheric convection modifications. It is shown that the conductivity profile predicted by the Sodankylä Ion and Neutral Chemistry (SIC) model does not shield the balloon payload at 32 km from the ionospheric horizontal field. Thus, the data really do indicate a very low level of ionospheric convection over the balloon during the 6 hours following the SEP event. We have used global magnetometer and satellite data to interpret the changes in the vertical field as indicators of large scale convection changes.

This paper applies statistical methods to interpolate missing values in a dataset of radiative energy fluxes at the surface of Earth. We apply Random Forest (RF) and seven other conventional spatial interpolation models to a global Surface Solar Radiation (SSR) dataset. We apply three categories of predictors; climatic, spatial, and time series variables. Although the first category is the most common in research, our study shows that it is actually the last two categories that are best suited to predict the response. In fact, the best neighboring variable is almost 40 times better than the best climatic variable in predicting SSR. Furthermore, our analysis shows that the Mean Absolute Error is 10.2 on average using RF, with a standard deviation of 1.5. Conventional methods have an average MAE of 21.3, with an average standard deviation of 6.4. This highlights the benefits of using machine learning in environmental research.

Orographic impact on extreme sub-daily precipitation is critical for risk management but remains insufficiently understood due to complicated atmosphere-orography interactions and large uncertainties. We investigate the problem adopting a framework able to reduce uncertainties and isolate the systematic interaction of Mediterranean cyclones with a regular orographic barrier. The average decrease with elevation reported for hourly extremes is found enhanced at sub-hourly durations. Tail heaviness of 10-minute intensities is negligibly affected by orography, suggesting self-similarity of the distributions at the convective scale. Orography decreases the tail heaviness at longer durations, with a maximum impact around hourly scales. These observations are explained by an orographically-induced redistribution of precipitation towards stratiform-like processes, and by the succession of convective cores in multi-hour extremes. Our results imply a breaking of scale-invariance at sub-hourly durations, with important implications for natural hazards management in mountainous areas.

2D-parametric model is used to simulate waves under Tropical Cyclones (TCs). Set of equations describing either wind waves development and swell evolution, is solved using method of characteristics. Wave-rays patterns provide efficient visualization on how wave trains develop and travel through TC varying wind field and leave storm area as swell. The superposition of wave-trains rays exhibits coherent spatial patterns of significant wave height, peak wavelength and direction, depending on TC characteristics, - maximal wind speed (um), radius (Rm), and translation velocity (V). Group velocity resonance leads to appearance of waves with abnormal energy between the TC right and front sectors, further outrunning as swell through the TC front sector. Yet, when TC translation velocity exceeds a threshold value, waves cannot reach group velocity resonance, and travelling backwards, form a wake of swell systems trailing the forward moving TC. 2D-parametric model solutions are parameterized using 2D self-similar universal functions. Comparisons between self-similar solutions and measurements, demonstrate excellent agreement to warrant their use for scientific and practical applications. Self-similar solutions provide immediate estimates of azimuthal-radial distributions of wave parameters under TCs, solely characterized by arbitrary sets of um, Rm and V conditions. Self-similar solutions clearly divide TCs between slow TCs fulfilling conditions Rm/Lcr>1, and fast TCs corresponding to Rm/Lcr <1, where Lcr is a critical fetch. The region around Rm/Lc = 1 corresponds to the group velocity resonance conditions, leading to the largest possible waves generated by a TC.

The rate of background seismicity, or the earthquakes not directly triggered by another earthquake, in active seismic regions is indicative of the stressing rate of fault systems. However, aftershock sequences often dominate the seismicity rate, masking this background seismicity. The identification of aftershocks in earthquake catalogs, also known as declustering, is thus an important problem in seismology. Most solutions involve spatio-temporal distances between successive events, such as the Nearest-Neighbor-Distance algorithm widely used in various contexts. This algorithm assumes that the space-time metric follows a bi-modal distribution with one peak related to the background seismicity and another peak representing the aftershocks. Constraining these two distributions is key to accurately identify the aftershocks from the background events. Recent work often uses a linear-splitting based on nearest-neighbor distance threshold, ignoring the overlap between the two populations and resulting in a mis-identification of background earthquakes and aftershock sequences. We revisit this problem here with both machine-learning classification and clustering algorithms. After testing several popular algorithms, we show that a random forest trained with various synthetic catalogs generated by an Epidemic Type Aftershock Sequence model outperforms approaches such as K-means, Gaussian-mixture models, and Support Vector Classifications. We evaluate different data features and discuss their importance in classifying aftershocks. We then apply our model to two different actual earthquake catalogs, the relocated Southern California Earthquake Center catalog and the GeoNet catalog of New Zealand. Our model capably adapts to these two different tectonic contexts, highlighting the differences in aftershock productivity between crustal and intermediate depth seismicity.