Land management activities that provide higher soil organic carbon stimulate microbial activity and enzymatic reactions. Riparian forest, agroforestry, and row-crop agriculture treatments are among common land-use systems in the lower Missouri River Floodplain (MRF) region in New Franklin, MO. The study of soil enzyme activities under different land use in this region is of importance for monitoring soil quality and evaluation of climatic changes on soil health. This investigation aimed to characterize soil properties such as soil C and N, porosity, moisture content under three-land use (agroforestry, riparian forest, and agriculture) and correlate their influence on soil microbial communities and enzyme activities. Soil samples were collected from the three land management systems, and enzyme activity was measured in three seasons of Fall 2019, Summer 2020, and Spring 2021. Results revealed significantly higher levels of β-glucosidase, β-glucosaminidase, and dehydrogenase activity in agroforestry (AF) and riparian forest (RF) treatments relative to agriculture (AG) management in all three studied seasons. Dehydrogenase activity was higher (p<0.0001) in RF relative to AF and AG sites. Efforts to incorporate perennial management systems in river-floodplain landscapes will help increase organic matter content, which stimulates microbial diversity and soil enzyme activity as well as improving the performance of conservation buffers. The study concluded that tree-based AF systems enhance soil physicochemical and biological properties.

Megathrust earthquakes impose changes of differential stress and pore pressure in the lithosphere-asthenosphere system that are transiently relaxed during the postseismic period primarily due to afterslip, viscoelastic and poroelastic processes. Especially during the early postseismic phase, however, the relative contribution of these processes to the observed surface deformation is unclear. To investigate this, we use geodetic data collected in the first 48 days following the 2010 Maule earthquake and a poro-viscoelastic forward model combined with an afterslip inversion. This model approach fits the geodetic data 14% better than a pure elastic model. Particularly near the region of maximum coseismic slip, the predicted surface poroelastic uplift pattern explains well the observations. If poroelasticity is neglected, the spatial afterslip distribution is locally altered by up to ±40%. Moreover, we find that shallow crustal aftershocks mostly occur in regions of increased postseismic pore-pressure changes, indicating that both processes might be mechanically coupled

Watershed hydrological processes controlled by subsurface structures that have hierarchical organization across scales, but there is a lack in multiscale model validation. In this study, using a comprehensive dataset collected in the forested Shale Hills catchment, we tested the series HYDRUS codes (i.e., HYDRUS-1D at the pedon scale, HYDRUS-2D at the hillslope scale, and HYDRUS-3D at the catchment scale) that included a hierarchical multi-dimensional modeling approach for water flow simulation in the vadose zone. There is good agreement between 1D simulations and measurements of soil moisture profiles controlled by soil hydraulic parameters and precipitation characteristics; however, short-term fluctuations in preferential flow were poorly captured. Notably, 2D and 3D simulations (Nash–Sutcliffe efficiency, ), which accounting subsurface preferential flow controlled by slope positions and shallow fractured bedrock, provided better results than 1D simulations (). Our modeling approach also illustrated that the studied watershed was characterized by weathered and un-weathered fracture bedrocks, which routed water through a network of subsurface preferential flow pathways to the first-order stream. Furthermore, a dual-porosity or anisotropy model produced more accurate predictions than a single-porosity or isotropy model due to a more realistic representation of local soil characteristics and layered structure. Our multi-dimensional modeling approaches credited with diagnosing and presenting the dominant hydrological processes and the interactions within soil-landscape features across one sloped catchment.

A few MHD simulations have been performed to study the interaction between a propagating interplanetary shock and Earth's standing bow shock, followed by the magnetosheath and magnetopause. They satisfactorily explain the deceleration of the interplanetary shock in the magnetosheath, which was observed a few dozen times by well-arranged satellites. In this work, we perform a hybrid particle-in-cell simulation in a similar setting with a self-consistently developed quasi-perpendicular interplanetary shock. We track the position of the interplanetary shock as it travels through the magnetosheath. We show that early in the propagation through the magnetosheath, the interplanetary shock slows down, which is in line with the conclusion of previous studies. Later in the propagation, however, we find that -- in the plane perpendicular to the interplanetary magnetic field -- the interplanetary shock travels faster inside the magnetosheath than it does in the solar wind. We also note that as the magnetic field lines are stretched along the magnetopause, the interplanetary shock becomes quasi-parallel close to the magnetopause.

Data-processing methods only considering the turn-off ramp in transmitting waveforms are well established for land-based transient electromagnetic (TEM) methods. The turn-on ramp effects on late-time responses are generally neglected by data-processing methods. Our forward-modeling results of homogeneous half-space models show that the full-waveform effect which includes both effects caused by turn-on and turn-off stages is common for a wide range of conductivities. The inversion results for synthetic and field examples illustrate that the inversion algorithm which does not consider the full-waveform effects can lead to a higher resistivity in deeper parts of recovered models compared with the true model. Therefore, it is of vital importance to account for the full waveform effects in both forward modeling and inversion algorithms. We use synthetic examples to show how our inversion algorithms can improve the recovered model, and use real-data examples to show the importance of incorporating full transmitting waveforms in data-processing procedures.

Accurate numerical modeling of fracture propagation and deflection in porous media is important in the development of geo-resources. To this end, we propose a novel modeling framework to simulate nonplanar three-dimensional (3-D) fracture growth within poroelastic media, using an iteratively coupled approach based on time-/scale-dependent fracture stiffness. In this approach, the propagating fractures are explicitly tracked and fitted at each growth step using triangular elements that are independent of the matrix discretized by hexahedral grids. The finite volume/finite element method (FVFEM) is employed to solve the hydro-mechanical system, based on the embedded discrete fracture model (EDFM). The calculated pressure in fractures and the stress state of the host grid of the embedded fractures constitute the boundary conditions for the boundary element method (BEM). The BEM module, in turn, renders the evolving fracture stiffness and aperture for the FVFEM module. Finally, the total stresses and the fracture-tip displacements are computed at the end of each time step to estimate the velocity and direction of newly created fractures ahead of the fracture tip. The proposed model is first validated against analytical solutions. Then, in three different examples, results are shown from the fracture’s footprint under layered stress conditions, simultaneous propagation of two nonplanar 3-D fractures, and the mechanical interaction of en échelon arrays. This work presents an efficient framework to simulate propagation of nonplanar fractures, and establishes the foundation to build an integrated simulator for fracture propagation, proppant transport, and production forecasting in unconventional formations.

A methane gas infilling fault theory is presented to refine and upgrade the conventional earthquake theory of elastic rebound of active geological faults. It adds the infilling of highly compressed methane gas mass into the deep geological fault zones. Such gas infilling makes the fault active and accumulate the elastic energy. With time, the gas in the fault traps has more and more mass and high and high pressure. The gas pressure can rupture the trap along the geological fault and release some gas mass to migrate along the fault, which causes tectonic earthquakes. The theory shows that the earth-quaking process is a cooling process because of gas expansion. The earthquake magnitude estimated from the seismic waves can have a quantitative relationship with the gas volume from the trap is quantified. The theory can explain mega earthquakes along subducton zones.

In this paper, we present the variations of IMF-Bz, Solar wind Parameters (Vsw, Nsw, and Psw), and Geomagnetic Indices (AE and SYM-H), and the variation of Vertical Total Electron Content (VTEC) using simultaneous VTEC data from 12 GPS-TEC stations over the Indian, Australian, Brazilian and South African regions. We describe contrast in Total Electron Content (TEC) throughout the globe using global ionospheric maps at regular 2-hour interval of UT during the three intense geomagnetic storms. Moreover, we observed that heavily TEC influenced areas were found to be transposing through equatorial plane starting from eastern sectors to the western sectors. Indian Ocean, Atlantic Ocean and South Pacific Ocean sectors were affected flowingly. Global Ionospheric Maps evince that Indian and Brazilian sectors were affected heavily explaining the TID and Equatorial Anomaly as seen on those areas. The equatorial and low-latitude regions have been mainly affected by the geomagnetic storms. All these results suggested that the acute disruption of global winds (surging towards the equator from higher latitudes) and electric fields commenced from magnetosphere-ionosphere interaction cause the severe modification in the equatorial, low-latitude region. We checked the cross correlation during the period of high solar and geomagnetic activities; the correlation gradually increased with the near by stations by latitudes in most of the cases which was another intriguing result. the storms were affected globally which is why we believe that variation of TEC over various stations of the globe could turn out to be very helpful in predicting solar wind coupling with magnetosphere-ionospehere.

There have been important criticisms of IPCC recent reports for failing to communicate the dire nature of the current predicament facing civilization – so-called “scientific reticence” – as well as for assuming functional, planetary-effective scale biomass carbon capture and storage in its survivable scenarios [1-3]. In the light of major reports released in 2018 [4,5] which underscore the discrepancy between the current climate trajectory and best-case requirements to maintain global civilization, the current predicament is often described as an “existential” crisis [6]. Part of the confusion appears to stem from the lack of discussion of specific scenarios, such as rapid arctic methane release [7,8], which are not discussed by the IPCC in proportion to their catastrophic potential. This scenario is briefly examined using the Goddard Institute for Space Studies (GISS) ModelE v2 7.50.05 [9]. It is suggested that the results presented here represent a lower bound to climate disruption since in this set-up, neither the oceans nor arctic sea ice (a significant and ongoing runaway feedback [10]) respond to the changes modeled; namely, a sudden release of stored methane gas.

Imagery from the GOES series has been a key element of U.S. operational weather forecasting for four decades. While GOES observations are used extensively by human forecasters for situational awareness, there has been limited usage of GOES imager data in numerical weather prediction (NWP), and operational data assimilation (DA) has ignored cloud and precipitating pixels. The motivation of this project is to bring the benefits of GOES-R Series enhanced capabilities to advance convective-scale DA for improving convective-scale forecasts. We have developed a convolutional neural network (CNN) prototype, dubbed “GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN)” that fuses GOES-R Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) information to produce maps of synthetic composite radar reflectivity. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. Making use of ABI spatial information potentially provides benefits over radiance assimilation approaches that are limited by saturation of radiances in pixels with precipitation. This presentation will briefly describe the GREMLIN model and characterize its performance across meteorological regimes. We are developing a dense neural network (DNN) to produce vertical profiles of radar reflectivity and latent heating based on the two-dimensional fields output from GREMLIN. The resulting three-dimensional fields of latent heating will be used to initialize NWP simulations of convective-scale phenomena. Another DNN will be developed to produce uncertainty estimates of latent heating for each pixel. Our approach for data assimilation will be described and is innovative in being the first-time machine learning (ML) will be used for the nonlinear latent heating observation operator in the NOAA hybrid Gridpoint Statistical Interpolation (GSI) and/or JEDI systems. The approach will provide all the elements of the Jacobian needed for GSI DA and has the advantage of automatically maintaining the tangent linear and adjoint models through finite difference mathematics.

Between 2012 and 2018 we mapped near-peak seasonal snow depths across two swaths covering 126 km in Northern Alaska using aerial structure-from-motion photogrammetry and lidar surveys. The surveys were validated by over a hundred thousand ground-based depth measurements. Using a quantitative method for identifying drift areas, we conducted a snowdrift census that showed on average 18% of the study area is covered by snowdrifts each winter, with 40% of the snow-water-equivalent contained in the drifts. Within the census we identified six types of drifts, some of which fill each winter, others which do not. The seasonal drift evolution was distinctly different in the two swaths, a result largely explained by physiographic differences. Using four metrics from the field of image quality analysis, we tested the year-to-year fidelity of these drift patterns, finding overall high year-to-year similarity (>70%), but with higher similarity values for filling drifts, and higher similarity in one swath vs. the other, again a function of the physiography. These high drift fidelity values are best explained by climatically convergent cumulative wind-blown snow fluxes interacting with drift traps to produce the same drifts year after year despite considerable differences in winter weather. However, due to the existence of filling vs. non-filling drifts, and a predicted increasing frequency of rain-on-snow events in the Arctic, future snowdrift patterns and drift evolution in the Arctic are likely to diverge from those of today.

Marine heatwaves (MHWs) are events of abnormally warm sea surface temperatures (SSTs) that can have devastating impacts on marine ecosystems and coastal economies. The evolution of these events depends partially on the local atmospheric response, and how changes in clouds and surface heat fluxes in turn affect SSTs. Understanding the role of the atmosphere in MHWs is essential for modeling and forecasting these events. Here we use satellite data from 2001-2019 to identify MHWs and anomalous atmospheric variables- including radiative heat fluxes, turbulent heat fluxes, and cloud cover- associated with these events. We find robust patterns in SST-cloud and SST-heat flux relationships that show important geographical differences in atmosphere-ocean interactions during MHWs. Because of these regional differences, we don’t expect MHWs to evolve the same way in all regions. We also find that the cloud response observed during MHWs globally corresponds well with the cloud response to future warming, as identified in the Cloud Feedback Model Intercomparison Project (CFMIP) ensemble of global climate models. This suggests that MHWs can provide valuable insight to anomalous atmosphere-ocean interactions under future warming.

We use densely spaced campaign GPS observations and laboratory friction experiments on fault rocks from one of the world’s most rapidly slipping low-angle normal faults, the Mai’iu fault in Papua New Guinea, to investigate the nature of interseismic deformation on active low-angle normal faults. GPS velocities reveal 8.3±1.2 mm/yr of horizontal extension across the Mai’iu fault, and are fit well by dislocation models with shallow fault locking (above 2 km depth), or by deeper locking (from ~5-16 km depth) together with shallower creep. Laboratory friction experiments show that gouges from the shallowest portion of the fault zone are predominantly weak and velocity-strengthening, while fault rocks deformed at greater depths are stronger and velocity-weakening. Evaluating the geodetic and friction results together with geophysical and microstructural evidence for mixed-mode seismic and aseismic slip at depth, we find that the Mai’iu fault is most likely strongly locked at depths of ~5-16 km and creeping updip and downdip of this region. Our results suggest that the Mai’iu fault and other active low-angle normal faults can slip in large (M > 7) earthquakes despite near-surface interseismic creep on frictionally stable clay-rich gouges.

Automatic identification of debris flow signals in continuous seismic records remains a challenge. To tackle this problem we use a machine learning approach, which can be applied to continuous real-time data streams. We show that a machine learning model based on the random forest algorithm recognizes different stages of debris flow formation and propagation at the Illgraben torrent, Switzerland, with an accuracy exceeding 90%. In contrast to typical debris flow detection requiring instrumentation installed directly in the torrent, our approach provides a significant gain in warning times of tens of minutes to hours. For real-time data streams from 2020, our detector raises alarms for all 8 independently confirmed Illgraben events and gives no false alarms. We suggest that our seismic machine-learning detector is a critical step towards the next generation of debris-flow warning, which increases warning times using both simpler and cheaper instrumentation compared to existing operational systems.

Geodetic fault slip inversions have been generally performed by employing a least squares method with a spatial smoothing constraint. However, this conventional method has various problems: difficulty in strictly estimating non-negative solutions, assumption that unknowns follow the Gaussian distributions, unsuitability for expressing spatially non-uniform slip distributions, and high calculation cost for optimizing many hyper-parameters. Here, we have developed a trans-dimensional geodetic slip inversion method using the reversible-jump Markov chain Monte Carlo (rj-MCMC) technique to overcome the problems. Because sub-fault locations were parameterized by the Voronoi partition and were optimized in our approach, we can estimate a slip distribution without the spatial smoothing constraint. Moreover, we introduced scaling factors for observational errors. We applied the method to the synthetic data and the actual geodetic observational data associated with the 2011 Tohoku-oki earthquake and found that the method successfully reproduced the target slip distributions including a spatially non-uniform slip distribution. The method provided posterior probability distributions with the unknowns, which can express a non-Gaussian distribution such as large slip with low probability. The estimated scaling factors properly adjusted the initial observational errors and provided a reasonable slip distribution. Additionally, we found that checkerboard resolution tests were useful to consider sensitivity of the observational data for performing the rj-MCMC method. It is concluded that the developed method is a powerful technique to solve the problems of the conventional inversion method and to flexibly express fault-slip distributions considering the complicated uncertainties.

The Koraput district is one of the most important centres for bauxite mining in Odisha. Bauxite deposits of Pottangi are located In Navdapur taluka going northwards towards Pottangi village which is a part of Koraput district,Odisha,situated on ‘Sirimanda Parbat’. The mines at Pottangi are under the charge of National Aluminium Company Limited (NALCO). The present study deals with the geostatistical modelling of the Central Block of the Pottangi bauxite deposit, Koraput district, Odisha while taking into account the various lithological structural and textural characteristics of the area. Exercises on population modelling showed that Al2O3, SiO2 and TiO2 exhibited skewed distributions while Fe2O3 displayed a symmetric distribution. Log-transformations were then performed on geo-variables displaying skewed distributions. The interrelationships and the degree of association among the variables have been established through bivariate analysis.3-D omni-directional experimental semi-variograms of Al2O3, SiO2, Fe2O3 and TiO2 were constructed at 50m lag interval. For these experimental semi-variograms spherical models were fitted through Point Kriging Cross-validation techniques using GEXSYS software. The entire deposit was divided into a number of blocks with optimum Selective Mining Unit. The result of block kriging was used to compute an ore inventory and to establish grade tonnage relationships estimating the quantity of bauxite ore and waste material, average grade of the ore and waste to ore ratio following a step-wise integration of the block grade frequency distribution of a range of grades. The following work done represents advanced geostatistical techniques for improved modelling of the Central Block of Pottangi Bauxite deposit, Koraput, Odisha. The geostatistical techniques used helps in a better evaluation of reserve estimates.

This paper presents a statistical analysis to investigate the day-to-day variability of field-aligned irregularities (FAI) occurrence in nighttime F-region ionosphere over the Equatorial Atmosphere Radar (EAR), West Sumatra, Indonesia. FAI echoes were identified based on signal intensity of backscatter radar observations. We analyzed nighttime F-region FAI during 3 years starting in January 2011 to December 2013. For the first time, a combinatorics analysis was applied to examine the statistical likelihood of various day-to-day FAI occurrence patterns. The empirical day-to-day combinatorics analysis was performed based on binary classification of EAR observation data into either FAI occurrence (+) or absence (-) for each calendar date. Permutations of various day-to-day occurrence patterns, from 1-day to 6-day patterns, were sorted into histograms. The combinatorics analysis was performed in 4 separate time intervals to account for seasonal variation: two equinoxes (March and September) and two solstices (June and December). EAR data show that FAI occurrence probability is maximum for the two equinoxes, and that it is minimum for the two solstices. Our analysis shows that certain day-to-day patterns are more likely to occur than others, and such “combinatorics fingerprints” depend on season. During the solstices, persistent absence of FAI over several consecutive days far outweighed persistent FAI occurrence over an equivalent grouping of days with the same length. Meanwhile, during the equinoxes, we found a generally more equitable distribution between persistent day-to-day FAI occurrence and persistent day-to-day FAI absence. These findings may open new ways to help forecast FAI occurrence on a regional basis.

Until the Mw 6.8 Elazığ earthquake ruptured the central portion of the East Anatolian Fault (EAF) on January 24, 2020, the region had only experienced moderate magnitude (Mw 6.2) earthquakes over the last century. Here, we use geodetic data to constrain a model of subsurface fault slip. We adopt an unregularized Bayesian sampling approach relying solely on physically justifiable prior information and account for uncertainties in both the assumed elastic structure and fault geometry. The rupture of the Elazığ earthquake was bilateral, with two primary disconnected regions of slip. This rupture pattern may be controlled by structural complexity. Both the Elazığ and 2010 Mw 6.1 Kovancılar events ruptured portions of the central EAF that are believed to be coupled during interseismic periods, and the Palu segment is the last portion of the EAF showing a large deficit of fault slip which has not yet ruptured in the last 145 years.