Permafrost degradation poses an increasingly serious threat of glacial lake outburst floodings (GLOFs) in the Tibetan Plateau. It is therefore of great practical significance to analyze the freeze-thaw state in moraine dams and associated impacts on dam stability. We simulated the soil temperature of the Longbasaba moraine dam based on the heat transfer module of COMSOL Multiphysics. The results show that the soil temperature of the moraine dam can be adequately simulated using the COMSOL Multiphysics heat transfer module and the simulated temperature values are generally consistent with the observed trends, yielding root mean square errors (RMSEs) less than 1.58 ℃ and Nash-Sutcliffe efficiency coefficients (NSEs) between 0.66 and 0.93. The average annual increase of the active layer depth was 0.026 m/a from 1959 to 2020. The buried ice inside the moraine dam was evidently melting and the maximum buried ice thawing depth under scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 in CMIP6 (Coupled Model Intercomparison Project Phase 6) is expected to be 11.3 m, 18.4 m, and 23.5 m, respectively, by the end of the century, which indicates a continuous deterioration of the moraine dam stability.

Non-ice impurities within the ice shells of ocean worlds (e.g., Europa, Enceladus, Titan) are believed to play a fundamental role in their geophysics and habitability and may become a surface expression of subsurface ocean properties. Heterogeneous entrainment and distribution of impurities within planetary ice shells have been proposed as mechanisms that can drive ice shell overturn, generate diverse geological features, and facilitate ocean-surface material transport critical for maintaining a habitable subsurface ocean. However, current models of ice shell composition suggest that impurity rejection at the ice-ocean interface of thick contemporary ice shells will be exceptionally efficient, resulting in relatively pure, homogeneous ice. As such, additional mechanisms capable of facilitating enhanced and heterogeneous impurity entrainment are needed to reconcile the observed physicochemical diversity of planetary ice shells. Here we investigate the potential for hydrologic features within planetary ice shells (sills and basal fractures), and the unique freezing geometries they promote, to provide such a mechanism. By simulating the two-dimensional thermal and physicochemical evolution of these hydrological features as they solidify, we demonstrate that bottom-up solidification at sill floors and horizontal solidification at fracture walls generate distinct ice compositions and provide mechanisms for both enhanced and heterogeneous impurity entrainment. We compare our results with magmatic and metallurgic analogs that exhibit similar micro- and macroscale chemical zonation patterns during solidification. Our results suggest variations in ice-ocean/brine interface geometry could play a fundamental role in introducing compositional heterogeneities into planetary ice shells and cryoconcentrating impurities in (re)frozen hydrologic features.

Water security is a global threat that is intensifying with increasing demands for freshwater and climate change. Scientific advances provide tools to address issues of water quantity and quality, but new advances in conflict resolution (CR) theory are also required. Here, we discuss new CR theory (conflict analysis as the intervention, and the use of middle-influencers as key players), along with a case-study of the application of these CR advances in a water-stressed region of Canada. We argue that continued adaptation, iteration, and refinement of CR theory involving both social and natural scientists is crucial for addressing water-related conflicts globally.

In seismology, the rupture mechanism of an earthquake, a glacier stick-slip and a landslide is not directly observed, but inferred from surface measurements. In contrast, laboratory experiments can illuminate near field effects, which reflect the rupture mechanism but are highly attenuated in the case of real-world surface data. We directly image the elastic wave-field of a nucleating rupture non-invasively in its near-field with ultrasound speckle correlation. Our imaging yields the particle velocity of the full shear wave field at the source location and inside the 3D frictional body. We experimentally show that a strong bimaterial contrast, as encountered in environmental seismology, yields a unidirectional or linear force mechanism for pre-rupture microslips and decelerating supershear ruptures. A weak contrast, characteristic for earthquakes, generates a double-couple source mechanism for sub-Rayleigh ruptures, sometimes preceded by slow deformation at the interface. This deformation is reproduced by the near field of a unidirectional force.

Tidal currents are known to influence basal melting of Antarctic ice shelves through two types of mechanisms: local processes taking place within the boundary current adjacent to the ice shelf-ocean interface and far-field processes influencing the properties of water masses entering the cavity. The separate effects of these processes are poorly understood, limiting our ability to parameterize tide-driven ice shelf-ocean interactions. Here we focus on the small-scale processes within the boundary current and we apply a one-dimensional plume model to a range of ice base geometries characteristic of Antarctic ice shelves to study the sensitivity of basal melt rates to different representations of tide-driven turbulent mixing. Our simulations demonstrate that the direction of the relative change in melt rate due to tides depends on the approach chosen to parameterize entrainment of ambient water into the plume, a process not yet well constrained by observations. A theoretical assessment based on an analogy with tidal bottom boundary layers suggests that tide-driven shear at the ice shelf-ocean interface enhances mixing through the pycnocline. Under this assumption our simulations predict an increase in melt and freeze rates along the base of the ice shelf when adding tides into the model. An approximation is provided to account for this response in basal melt rate parameterizations that neglect the effect of tide-induced turbulent mixing

Emissions of methane (CH4) in the Permian basin (U.S.A.) have been derived for 2019 and 2020 from satellite observations of the Tropospheric Monitoring Instrument (TROPOMI) using the divergence method, in combination with a data driven method to estimate the background concentrations. The resulting CH4 emission data, which have been verified using model with known emissions, have a spatial resolution of approximately 10 km. The spatial patterns of the emissions are in a good agreement with the locations of oil and gas production and drilling activities in the Permian basin, as well as with emissions of nitrogen oxides (NOx). Analysis of time-series of locations with large CH4 emissions indicated that there are significant continuous emissions in this region. The CH4 emissions can be characterized as a continuous area source, rather than as dominated by a few large unplanned releases. This is important considering possible CH4 emission mitigation strategies. In addition to providing spatially resolved emissions, the divergence method also provides the total emissions of the Permian basin and its main sub-basins. The total CH4 emission of the Permian is estimated as 3.0 ± 0.7 Tg yr-1 for 2019, which agrees with other independent estimates based on TROPOMI data. For the Delaware sub-basin, it is estimated as 1.4 ± 0.3 Tg yr-1 for 2019, and for the Midland sub-basin 1.2 ± 0.3 Tg yr-1. In 2020 the emissions are 8% lower compared to 2019, which could be a result of strong decreases in drilling activities due to the COVID-19 crisis.

Different ionospheric climatological models such as NeQuick2 and NTCM have been developed to mitigate the ionosphere impact on the trans-ionosphere propagating radio wave. Moreover, the Neural Network (NN) is used to model and characterize the ionosphere. However, no one has compared the performances of NeQuick2, NTCM, and NN after adapting to GPS TEC. This study evaluates their performances in the East-African region in 2013 and 2015. It has been done by computing their drivers (effective ionization level, Az for NeQuick2 and ionization driving index, Id for NTCM) through least-square fitting to TEC observation sense. NN-algorithm has also been trained and tested with observed TEC used for NeQuick2 and NTCM adaptation. The annual performance test has shown that the correlation coefficient (R) values between observed and NTCM modeled TEC, after an adaption, are better than the corresponding values obtained from NeQuick2 and NN. It also shows that the standard deviations (STD) and root-mean-square errors (RMSE) obtained for NTCM are smaller than the STD and RMSE computed for NeQuick2 and NN. On the other hand, the daily performance test of now-casting and predicting showed that the NN performs the best, followed by NTCM. However, the 1-hour prediction test showed that NTCM performs the best among the models considered in this study.

High resolution maps of seismic attenuation parameters in Taiwan have been obtained by using a modified ”Multiple Lapse Time Window Analysis’ (MLTWA). At most of the stations in porous sedimentary and highly faulted areas in Taiwan, the conventional modeling of MLTWA based on the scalar theory of radiative transfer in a half-space with isotropic scattering fails to explain the spatio-temporal distribution of the whole S wavetrain. Using Monte Carlo simulations of wave transport, we demonstrate that this anomalous energy distribution in the coda may be modelled by multiple anisotropic scattering of seismic waves. In addition to the scattering quality factor Qsc, we introduce a parameter g (independant of Qsc) which determines the angular redistribution of energy upon scattering (scattering anisotropy). We determine the attenuation parameters Qsc-1, Qi-1 and g in three frequency bands (1-2, 2-4 and 4-8Hz). Overall, Taiwan is more attenuating than most orogens with a mean effective scattering loss (Qsc*)-1=Qsc-1(1-g) about 0.025 and a mean intrinsic absorption Qi-1 about 0.009 at 1.5Hz. Scattering loss (Qsc*)-1 varies over more than one order of magnitude across Taiwan while absorption fluctuations are about 30%. The more attenuating zones are the Coastal Range and the Coastal Plain where scattering dominates over absorption at low frequency, and inversely at high frequency. These regions are also characterized by strong backscattering (g<-0.85) at 1.5Hz and rather high VP/VS ratio. We speculate that the observed strong back-scattering at low frequency is related to strong impedance fluctuations in the crust induced by the presence of fluids.

A short watercolor comic about the broken connection between humans and mountains. Funded by Sharing Science grants of the AGU, 2021.

To determine a paleopole, the paleomagnetic community commonly applies a loosely defined set of quantitative data filters that were established for studies of geomagnetic field behavior. These filters require costly and time-consuming sampling procedures, but whether they improve accuracy and precision of paleopoles has not yet been systematically analyzed. In this study, we performed a series of experiments on four datasets which consist of 73-125 lava sites with 6-7 samples per lava. The datasets are from different regions and ages, and are large enough to represent paleosecular variation, yet contain demonstrably unreliable datapoints. We show that data filters based on within-site scatter (a k-cutoff, a minimum number of samples per site, and eliminating the farthest outliers per site) cannot objectively identify unreliable directions. We find instead that excluding unreliable directions relies on the subjective interpretation of the expert, highlighting the importance of making all data available following the FAIR principles. In addition, data filters that eliminate datapoints even have an adverse effect: the accuracy as well as the precision of paleopoles decreases with the decreasing number of data. Between-site scatter far outweighs within-site scatter, and when collecting paleomagnetic poles, the extra efforts put into collecting multiple samples per site are more effectively spent on collecting more single-sample sites.

At least five surface rupturing earthquakes that occurred during a <300 year time span near Carson City, Nevada form a spatiotemporal cluster of earthquakes similar to those observed on fault systems around the world. These earthquakes exhibit not only temporal clustering behavior, but also have varying rupture boundaries during successive earthquakes. The Carson Range Fault System is a series of east-dipping normal faults that extend ~100 km southwards from Reno, Nevada. Previously published paleoseismic and lidar data spanning this system provide evidence of six surface rupturing earthquakes that occurred across the Carson Range Fault System during the last 2500 years. The three most recent of these earthquakes occurred from 800-500 cal. ybp, and two other earthquakes occurred on the nearby Incline Village and East Carson Valley faults during this time period. Together these five M6.5-7.1 earthquakes form a spatiotemporal cluster or supercycle.

Geologists need help classifying microscope rock images of sigma clasts; a type of mantled porphyroclasts widely used as kinematic indicators in rocks. Knowledge about the shear sense of sigma clast during formation (either CCW or CW shearing) gives insights into rock formation history. This work reports on early investigation of machine learning techniques for automatic detection and classification of sigma clasts and their rotation from photomicrographs. Convolutional Neural Networks (CNNs) are used to extract and leverage defining features of sigma clasts, such as shape, color, texture, and tail direction to improve accuracy. We leverage existing models that are pre-trained on very large collections of images, and use transfer learning techniques to apply them to microstructure images. We used YOLOv3 to identify different sigma clasts in a given image. We also experimented with other large pre-trained models such as ResNet50, VGG19, InceptionV3 with two additional layers trained specifically on our dataset. In order to facilitate exploration of different models with different settings, we are developing a computational experimentation environment to visualize different CNN network layers, classification heatmaps, and comparative metrics. Finally, since models perform better when more data are available, we are developing a web application to collect additional data from geoscientists and incentivize their participation in open science. The website allows researchers to upload images of rock microstructures, showing them the classification of the images based on the best models available, and allows them to correct any errors which can be used to improve the models.

Underground monitoring of root morphology and their interactions with the environment is critical to understand the overall performance of a plant. Such understanding allows plant breeders to develop plants that are resilient to the adverse effects of climate change and potentially even improve yields for food, fuel, and fiber. We propose and experimentally demonstrate the use of fiber Bragg grating-based fiber optic sensors as a non-destructive technology to measure width and depth of a root-like object and monitor the change in groundwater level as an indicator for soil-water content. Low-cost and continuous remote monitoring analyzed the spectral shift induced optical power change in the fiber optic sensors.

We present several methods for improving plant reconstruction from multiple 3D observations. Producing 3D data useful for plant phenotyping requires proximal sensing (e.g. line scanner, depth camera) at multiple incident angles (φ) and often with multiple passes. These resulting individual point clouds must then be assembled into a single point cloud for analysis. Our interest in improving the registration of individual plants is focused specifically on observations made within field settings which present additional challenges over laboratory 3D scans, where background, overlap and light conditions can be controlled. To develop these methods, we use several season’s worth of data from the University of Arizona’s Field Scanalyzer located in Maricopa, Arizona. Our approach prioritizes: (1) plant completeness, (2) noise reduction, (3) temporal similarity and (4) computational efficiency. The first priority is accomplished simply by prioritizing individual point clouds that contain the majority of the individual plant. 3D field scanning can result in component point clouds that are from near-identical φ and cover the same portions of the individual plant. This results in both additional noise and uncertainties due to small georeferencing errors and plant movement between scans. Thus, we remove the data that is furthest in time with non-unique φ in order to achieve priorities 2 and 3. Our method results in small scene reconstruction which has low memory and computational demands. In order to improve registration further, we investigate iterative closest point (ICP) registration fitting using weights defined by crop height distributions and semantic segmentation point labeling.

Previous crop yield improvements have been largely due to the implementation of new management strategies, mechanization, and application of emerging technologies. While these approaches have led to stable, linear improvements, increases in crop yields are currently plateauing. The use and improvement of rapid, automated, and accurate phenomic selection methods leveraging high-resolution data collected throughout a growing season could help identify stress-adaptive traits to meet the growing global food demand. As the capacity of phenomics to generate larger and higher dimensional data sets improves, there is an urgent need to develop and implement robust and scalable data processing pipelines for rapid turnaround of processed results. Current phenomics processing pipelines lack modularity and the ability to exploit the distributed computational infrastructure required for machine learning (ML)-based workloads. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines that aim to improve data processing efficiency for plant science research. PO integrates open-source frameworks for distributed task management on local, cloud, or high-performance computing (HPC) systems. Each pipeline component is available as a standalone container which can be independently deployed or linked into a pipeline. Additionally, researchers can swap between available containers or integrate new ones suited to their specific research. PO extracts phenotype trait values such as volume, height, canopy temperature, and maximum quantum efficiency (F v /F m) of photosystem II from data captured in field settings, enabling the study of phenotypic variation for elucidation of the genetic components of quantitative traits.

Abstract It has been debated globally that the COVID-19 lockdown had significantly diminished the emission levels of anthropogenic greenhouse gases (GHGs). However, different countries possess different footprints of GHGs emission. In regions with inconsistent air quality observation, spaceborne sensors can provide synoptic assessment of air quality with time-based environmental decision making. In this study, we utilised satellite data to quantify the temporal dynamics of carbon monoxide (CO) and nitrogen dioxide (NO2) between the pre-lockdown (January–March 2020), lockdown (April–July 2020) and post-lockdown (August–September 2020) periods in Nigeria. Periodic TROPOspheric Monitoring Instrument (TROPOMI) datasets were acquired from the Google Earth Engine Sentinel-5 Explorer and the Copernicus Open Access Hub. The Population-Weighted Mean Concentration (PWEC) of CO and NO2 was computed using raster-based population data and place-based air quality estimates. The associated economic correlates were computed using data mined from TROPOMI and available health records of Nigeria. Satellite data analysis showed that aggregate CO reduced by 35.1% (25.32⋅105 tons) and 9.06% (6.54⋅105 tons) and NO2 plummeted by 32.81% (22,500 tons) and 11.63% (5,360 tons) during the lockdown and post-lockdown periods across the 36 States of the country. While mobility rate dwindled substantially, mortality rate savings from the exposure to damaging effects of the GHGs were roughly $ 14 million (CO) and $10 million (NO2). The fluxes in CO and NO2 suggest that anthropogenic interference in air quality accounting can aid the understanding of the convoluted human–nature relationships for sustainable environmental management.

Achieving high accuracy for Global Navigation Satellite Systems (GNSS) positioning at low cost, is the main reason for the wide acceptance that has received the technique of Precise Point Positioning (PPP) from the geodetic community. However, the long convergence time required to achieve centimeter-level accuracy in positioning, remains of PPP’s main disadvantage. Our proposed method is aiming to provide accurate prediction of ionosphere variations at regional level, in a form suitable to be applied in PPP processing as external ionospheric information, so that to successfully deal with the ionospheric error effects. Machine Learning regression-based approaches for sequence modeling are suitable for predicting the ionospheric variability. Gaussian Process Regression (GPR) and Support Vector Regression (SVR) are introduced for ionospheric variations modeling at different locations of the International GNSS Service (IGS) network. The proposed algorithms predict the future electronic content per satellite from a specific station, fitting a non-linear model. The evaluation of our proposed methods for vertical total electron content (VTEC) values prediction is compared against the traditional Autoregressive (AR) and Autoregressive Moving Average (ARMA) methods, per satellite. Additional, mean TEC timeseries are compared against classical Global Ionospheric Maps (GIM), NeQuick and International Reference Ionosphere (IRI) models.

Afforestation and land use changes have turned Southern China into one of the largest carbon sinks globally, which sequesters carbon from the atmosphere thus mitigating climate change. However, forest growth saturation and available land that can be forested limit the longevity of this carbon sink, and while a plethora of studies have quantified vegetation changes over the last decades, the remaining carbon sink potential of this area is currently unknown. Here, we train a model with multiple predictors characterizing the heterogeneous landscapes of Southern China and predict the carbon carrying capacity of the region for 2002-2017. We compare observed and predicted carbon density and find that during two decades of afforestation, 2.34 Pg C have been sequestered between 2002 and 2017, and a total of 5.32 Pg carbon can potentially still be sequestrated. This means that the region has reached 75% of its carbon carrying capacity in 2017, which is 12% more than in 2002, equal to a decrease of 0.83% per year. We identify potential afforestation areas that can still sequester 2.39 Pg C, while old and new forests have reached 87% of their potential with 1.85 Pg C remaining. Our work locates areas where vegetation has not yet reached its full potential but also shows that afforestation is not a long-term solution for climate change mitigation.