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.

Global navigation satellite systems (GNSS) or satellite navigation is an important technological advancement; however, it is greatly impacted by the effects of space weather, such as ionosphere scintillation. Ionosphere scintillation is one of the causes of errors in the GNSS signals and also has the potential to cause a loss of access to GNSS. Ionosphere scintillation often impacts the polar region; however, the cause is not always known. One potential source of scintillation is polar cap patches. In Ren et al., [2018], a polar cap patch database was created based on the incoherent scatter radar measurements at Resolute Bay (RISR). Using data provided by the CHAIN Network of ionosphere scintillation detected near Resolute Bay in 2016, it can be determined how polar cap patches impact ionosphere scintillation. A statistical analysis as well as event analysis have been performed. Scintillation data from GNSS satellites with an elevation angle over 40 degrees were collected from each patch in the database and were compared to daily average. It was found that statistically there is no obvious phase scintillation or amplitude scintillation increase associated with patch in the polar cap. For the event analysis, three different patch events with and without enhanced scintillation were chosen for in-depth analysis and cross-comparison. Other datasets, including AMPERE FAC and RISR, are used to understand the plasma characteristics and geomagnetic activity conditions during these events.

We present our new multiscale pairwise-force smoothed particle hydrodynamics (PF-SPH) model for the characterization of flow in fractured porous media. The fully coupled multiscale PF-SPH model is able to simulate flow dynamics in a porous and permeable matrix and in adjacent fractures. Porous medium flow is governed by the volume-effective Richards equation, while the flow in fractures is governed by the Navier–Stokes equation. Flow from a fracture to the porous matrix is modeled by an efficient particle removal algorithm and a virtual water redistribution formulation to enforce mass and momentum conservation. The model is validated by (1) comparison to a finite element model (FEM) COMSOL for Richards-based flow dynamics in a partially saturated medium and (2) laboratory experiments to cover more complex cases of free-surface flow dynamics and imbibition into the porous matrix. For the laboratory experiments, Seeberger sandstone is used because of its well-known homogeneous pore space properties. The saturated hydraulic conductivity of the permeable matrix is estimated from a pore size and grain size distribution analysis. The developed PF-SPH model shows good correlation with the COMSOL model and all types of laboratory experiments. We employ the proposed model to study preferential flow dynamics for different infiltration rates. Here, flow in fracture is associated with the term “preferential flow”, providing rapid water transmission, while flow within the adjacent porous matrix enables only slow and diffuse water transmission. Depending on the infiltration rate and water inlet location, two cases can be distinguished: (1) immediate preferential/fracture flow or (2) delayed preferential flow. In the latter case, water accumulates at the surface first (ponding), then the fracture rapidly transmits water to the bottom system outlet. For the immediate fracture flow response, ponding only occurs once the fracture is fully saturated with water. In all cases, preferential flow is much more rapid than diffuse flow even under saturated porous medium conditions. Furthermore, infiltration dynamics in rough fractures adjacent to an impermeable or permeable matrix for different infiltration rates are studied as well. The simulation results show a significant lag in arrival times for small infiltration rates when a permeable porous matrix is employed, rather than an impermeable one. For higher infiltration rates, water rapidly flows through the fracture to the system outlet without any significant delay in arrival times even in the presence of the permeable matrix. The analysis of the amount of water stored in permeable fracture walls and in a fracture void space shows that for small infiltration rates, most of the injected water is retarded within the porous matrix. Flow velocity is higher for large infiltration rates, such that most of the water flows rapidly to the bottom of the fracture with very little influence of matrix imbibition process

Numerous wetlands in North America’s Prairie Pothole Region (PPR) provide important ecosystem services to surrounding areas, yet are threatened by climate and land-use changes. Understanding the impacts of climate change on prairie wetlands is critical to effective conservation planning. In this paper, we construct a wetland model with surface water balance (soil water content) and ecoregions and apply it to predict future wetland distribution under a climate change scenario. The future climate forcing is from a dynamical downscaling approach of a high-resolution convection-permitting regional climate model. The results show that the impacts of climate change on wetland extent are spatially heterogeneous and seasonally varied. The future wetter climate in the western PPR will favor increased wetland abundance in both spring and summer. In the eastern PPR, particularly in the moist mixed grassland and aspen parkland, the wetland area will increase in spring but experience enhanced declines in the summer due to strong evapotranspiration. When combined with historical patterns of anthropogenic drainage, results suggest that diverse conservation and restoration strategies will be needed. The outcomes of this study will be useful to conservation agencies to ensure that current investments will continue to provide good conservation returns in the future.

This paper reports a new and significantly enhanced analysis of US flood hazard at 30m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level and inclusion of the impact of local interventions in the flooding system. For the first time we consider pluvial, fluvial and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centred on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high quality local models and the entire catalogue of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69-0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near future time horizon we project clear patterns of changing flood hazard (-3.8 to +16% changes in 100yr inundated area at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defence protection may become compromised by this time.

Previous results indicate that the global hydrological cycle is more sensitive to Solar Radiation Modification (SRM) than is the surface temperature. Thus, it is expected that restoring temperature with SRM would decrease evaporation and precipitation. However, here we show that a more complete radiative antidote to CO2 can be obtained by spectrally tuning the SRM intervention, reducing insolation at some wavelengths more than others. By concentrating solar dimming at near-infrared wavelengths, where H2O has strong absorption bands, the direct effect of CO2 on the tropospheric energy budget can be offset, which minimizes perturbations to the hydrological cycle. Idealized cloud-resolving simulations of radiative-convective equilibrium confirm that spectrally-tuned SRM can simultaneously maintain surface temperature and precipitation at their unperturbed values even as large quantities of CO2 are added to the atmosphere. These results illuminate the connection between the spectral properties of SRM interventions and their potential impacts on precipitation.

The Spacecraft Interaction Plasma Software package (SPIS), a three-dimension particle in cell (PIC) code, was used to model the Parker Solar Probe (PSP) spacecraft and FIELDS instrument and their interactions with the Solar wind. Our SPIS modeling relied on material properties of new spacecraft materials that we had obtained in previous work. The model was used to find the floating potentials of the spacecraft and FIELDS antennas at different distances from the Sun (from 1AU to 0.046AU). We find the following results: At greater distances from the Sun, the shadowed spacecraft charges negatively while the illuminated Thermal Protection System (TPS) charges positive due to the high resistance of the TPS Alumina shield at low temperatures. As the spacecraft approaches the Sun, the temperature of the TPS increases, the resistance between it and the spacecraft drops, and its photoemission increases, driving the spacecraft more positive. At the same time, an electrostatic barrier forms near the illuminated surface of the TPS and reflects the photoelectrons back leading to negative charging of some surfaces. The FIELDS antennas and shield also see this barrier forming but on a smaller scale. The FIELDS antennas charge positively at all distances modeled when no current bias is applied. Current biasing of the antennas affects their floating potential.

We develop a rate- and state-dependent friction (RSF) model to investigate a compendium of recent experiments performed in the laboratory. In the documented experiments, a fault was sheared until macroscopic stick-slip frictional failure. Before macro-failure, small precursor seismicity nucleated from regions that also experienced aseismic slow slip. This behavior requires heterogeneity and is defined in our model as local variation in frictional parameters inferred from the roughness. During sliding wear introduced a smooth-polished surface onto a previously rough surface and was quantified using a bimodal Gaussian distribution of surface heights. We used spatial distribution of the smooth and rough sections to impose binary partitioning in critical slip distance $D_{c}$ to a planar frictional model. Simulations revealed that local seismicity nucleated on the “smooth’ sections, while the larger “rough’ section hosted aseismic slip. As the level of heterogeneity between smooth and rough sections increased, the model transitioned from a predominantly stick-slip to creeping. The simulations produced a dominant asperity, which appeared to control aspects of rupture nucleation: ($i$) weak heterogeneity caused the dominant asperity to generate foreshocks but also “ignite’ cascade-up fault-wide event, while ($ii$) strong heterogeneity led to constrained repeaters. Seismic source properties: average slip $\delta$, seismic moment $M_{0}$, stress drop $\Delta \tau$ and fracture energy $G^{’}$, were determined for each event and agreed with separate kinematic estimates made independently from seismic measurements. Our numerical calculations provide insight into rate-dependent cascade-up nucleation theory where frictional heterogeneity here was associated with wear of solid frictional contacts in the laboratory.

This research shows Part II of the Spacecraft Interaction Plasma Software (SPIS) used to model the Parker Solar Probe (PSP) FIELDS instrument and its interactions with the Solar Wind. Flight data was used to run the PSP model and compared with models using past predicted parameters. The effect of voltage biasing between the antenna, its shield, and the spacecraft on the current balance of each surface was investigated at first perihelion (0.16AU). The model data was reduced to I-V curves to find current saturations (analysis results 52µA versus flight results 54-72 µA), and sheath resistances (analysis results of 307 kΩ versus flight results of 51 kΩ). The recommended bias current to ensure optimal sensitivity of the FIELDS antenna was between -52 µA and -22 µA, which corresponded to a differential potential with respect to the spacecraft between -5V and 5V. The analysis also shows that plasma sheath of the FIELDS antenna and the plasma sheath of the FIELDS shield interacted between each other with an impedance of ~220kΩ.

Skillful forecasts of daily streamflow on river networks are crucial for flood mitigation, especially in rainfall-driven river basins. This is of acute importance on the Narmada River Basin in Central India, which is driven by the summer monsoon rainfall, and floods lead to heavy loss of life and infrastructure. Physical hydrologic models based on the land surface model – Variable Infiltration Capacity (VIC), have been developed in an experimental mode to model and forecast the hydrologic system, which includes – storage, soil moisture and runoff – by incorporating rainfall data from the India Meteorological Department (IMD). To enhance the forecast skill by model-combination, we propose a coupled physical-statistical modeling framework. In this, we couple the VIC model with a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow forecasts that uses the network topology to capture the spatial dependence. The daily streamflow at each station is modelled as Gamma distribution with time-varying parameters. The distribution parameters for each day are modeled as a linear function of covariates, which include antecedent streamflow from upstream gauges and, daily 2-day, or 3-day precipitation from the upstream contributing areas - that reflect the antecedent land conditions. The posterior distribution of the model parameters and, consequently, the predictive posterior distribution of the daily streamflow at each station and for each day are obtained. To the BHNM model, we will couple the VIC model by including the hydrologic forecasts – especially soil moisture and storage – as additional covariates. The coupled model will be demonstrated by its application to daily summer (July-August) streamflow at 4 gauges in the Narmada basin network for the period 1978 – 2014. The skill of the probabilistic forecast will be assessed using rank histograms and skill scores such as CRPS and RPS. The model skill will also be tested on five high flooding events on both the timing and magnitude. These model combinations will enable to combine the strengths of the individual models in capturing the hydrologic processes, biases and nonstationary relationships, to provide skillful daily streamflow forecasts. This will be of immense help in flood mitigation.

The solar quiet variation (Sq) observed at observatories near the latitude of the Sq vortex focus is difficult to assess because it is affected by both the geomagnetic activity level and the dynamics of the ionosphere and the upper atmosphere resulting in changes of the Sq ionospheric vortex shape and position. The use of only geomagnetically quiet days (QD) to calculate Sq for a given month can, to a certain extent, remove the effect of geomagnetic disturbances; however, the effect of the atmospheric dynamics still needs to be taken into account. The Sq vortex shape and position can be acquired from the horizontal vector of the geomagnetic field measured at geomagnetic observatories located in the European-African sector between 10ºN and 60ºN using vector rotation or calculating equivalent currents. Here we present results of a comparative analysis of two methods to extract Sq variations from the observations of the earth geomagnetic field. The analyzed data are measurements of the geomagnetic field done between 2007 and 2017 at the Coimbra Geomagnetic Observatory (COI, Portugal) located near 40ºN.The principal component analysis (PCA) based Sq curves are compared with the standard ones obtained using 5 international QD per month. For most of the analyzed years for the X component, the second PCA mode was identified as Sq variation whereas for the Y and Z components for all analyzed data sets the first PCA mode was identified as Sq variation. We studied differences and similarity of the PCA and IQD based Sq in relation to (1) the average geomagnetic activity level and (2) Sq vortex shape and position relatively to COI.