Excess sediment is one of the main agents of water pollution, affects the aquatic ecosystem and causes siltation problems in reservoirs and rivers. Bottom sediments are coarse and easily deposited, being the major responsible for morphological alterations in rivers. Therefore, is important to estimate the amount of sediment transported in other to establish mitigation strategies and recovery of affected areas. There are several methodologies for estimating bedload transport: equations, numerical modeling, direct physical sampling, and indirect method measuring dune tracking. Most studies measure bedload transport using the equipment Helley-Smith, generally taken as reference value for other methods. However, Helley-Smith causes changes in the flow field, often resulting in overestimates in sand bed rivers. Those overestimates were not yet described and quantified in detail. This article tries to solve this gap by observing the Helley-Smith sampler at the time of sampling. For this, we used a video camera recording processed by Particle Image Velocimetry (PIV) technique to qualitatively evaluate changes that the intrusive sampler causes in the displacement of bottom sediments in a sand bed river. When the sampler was properly allocated to the bottom, PIV resulting velocity maps of bedload particle transport identified the acceleration of the bottom particles as they approached the mouth of the sampler. We also noticed areas of convergence and vorticity of the sediments towards the sampler’s mouth, which may overestimate sampling. When the sampler’s mouth was disconnected from the bed due to the morphology of the bottom, we find regions of divergence in the displacement of the sediments creating unrealistic results for bedload transport analysis. In addition, the presence of organic matter can be underestimated, which causes difficulties in analyzing the quality of the collections because it is not possible to guarantee an equity in the positioning of the equipment at the bottom of the channel. We thus recommend complementation Helley-Smith measurements with surrogate methods, for example such as using ADCPs or optical methods, as the one described here.

Wind is an important driver of large-scale ocean currents, imparting momentum into the ocean at the sea surface. In particular, strong westerly winds help to drive the Antarctic Circumpolar Current, which of key importance for the global climate system. Over the past decades observations established that the strength of the westerlies over the Southern Ocean has increased as a result of climate change forcing. This increase is consistent with global climate model simulations. The future climate state depends strongly on how will the Antarctic Circumpolar Current respond to this strengthening. Eddy saturation is a theoretical regime where the transport of the current remains insensitive to the strengthening of the westerlies. Instead, the strengthening of the westerlies energizes transient eddies. Both satellite observations and numerical simulations suggest that the Antarctic Circumpolar Current is close to the eddy saturated limit. Traditionally eddy saturation has been attributed to baroclinic processes, but recent work suggests that barotropic processes that involve, e.g., standing meanders of the Antarctic Circumpolar Current, can also be responsible for producing eddy-saturated states. Here, we discus the different physical entities of the“usual” baroclinic eddy saturation as well as the recent notion of barotropic eddy saturation. We assess the relative importance of barotropic and baroclinic processes in producing eddy-saturated states using numerical simulations of primitive equations in an idealized setup. Lastly, we discuss potential implications these processes have on global ocean modeling.

NOAA/National Ocean Service’s (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) provides users with real-time currents at more than sixty stations, currents predictions at thousands of historic and active stations, and operational currents nowcast and near-term forecast information in navigable estuaries, harbors, rivers, and coastal regions of the United States as an integral part of its safe navigation mission. CO-OPS performs at least one current survey each year to update currents predictions in regions of particular interest to stakeholders. NOS’ Operational Forecast Systems (OFS) operate and maintain fully 3-D hydrodynamic models that provide regional nowcast and forecast (48-60 hours) guidance for water levels, currents, salinity, and temperature. By combining the information available from real-time observations, current surveys, historic current predictions, and OFS output, both the tidal current predictions and the OFS forecast products can be improved. Tidal current predictions are traditionally generated based on analysis of at least one month of six-minute observation data obtained from a physically deployed current meter using harmonic least squares techniques to resolve tidal constituents. By using longer time series and traditional techniques combined with additional statistical methods, a more accurate representation of the tidal flow and a more exact match to the shape of the tidal current curve is obtained. By applying these methods to long-term OFS output at locations where long-term physical observations also exist, tidal current predictions based on model data within the model domain can be verified. Once verified, the number of available “virtual stations” exponentially increases the repository of tidal current predictions. Users can choose any location of interest and receive tidal current predictions that can be incorporated into integrated, user-friendly navigation products and tools. We will evaluate this technique in the lower Chesapeake Bay using output from the Chesapeake Bay Operational Forecast System (CBOFS) and real-time currents data.

Permafrost regions store an estimated half of the global belowground organic carbon pool and twice the global atmospheric carbon level. A warming climate results in increased carbon gas emission, therefore knowing more about the amount and composition of organic carbon stored in permafrost regions is crucial for understanding feedbacks on global climate change. Using the Yukon-Kukskowim (YK) Delta, Alaska as a study site, we quantified belowground carbon pools and their potential vulnerability to release into the atmosphere as greenhouse gasses. We identified relevant landcover classes (burned and unburned upland peat plateaus, wetlands, ponds/lakes) in the YK Delta, from which we quantified total belowground carbon pools (30cm) and assessed the composition of the organic matter using Fourier-transform infrared spectroscopy. To characterize the size and distribution of soil carbon pools in the YK Delta, we built a Random Forest Machine Learning model that mapped the spatial distribution of soil carbon to a depth of 30 cm over a 1910 km2 watershed. The map product was produced in Google Earth Engine and used covariates that include, but are not limited to, Worldview2 high-resolution optical imagery (2m), ArcticDEM (5m), and Sentinel-2 level 1C multispectral imagery (10 m), including NDVI. We found substantial variation across landcover classes in soil characteristics that affect organic matter vulnerability, including gravimetric water content, thaw depth, bulk density, and percent carbon. Compared to upland areas, thaw depths were significantly deeper in wetlands and lakes, where we detected no surface permafrost (to 1m). Soil carbon content (%) was greatest in moss-dominated wetlands; however, these areas also had the lowest bulk density. Carbon pools and organic matter characteristics also varied between burned and unburned areas. Therefore, we expect that carbon vulnerability varies by landcover class and that future carbon emissions are driven by total carbon pools, thaw depths, and composition of the carbon stored in organic matter pools. These carbon pool and vulnerability maps will contribute to better understanding the impacts of subarctic warming and are critical for developing a more accurate assessment of carbon cycling feedbacks from permafrost regions on global climate change.

Climate change is a primary factor influencing alterations in watershed hydrology. Associated changes in temperature and precipitation can influence the fate and transport of non-point source pollution within a watershed, which complicates the application of best management practices (BMPs) for pollution mitigation. Understanding the sensitivity of BMP implementation as climate change is critical for proper management of water resources. The objective of this study is to understand the effects of BMPs on sediment and nutrient yields in the Meramec River watershed in eastern Missouri, U.S.A due to changes in climate. The Soil and Water Assessment Tool (SWAT) was used to model the flow, sediment and nutrient yields across the watershed. Multi-site calibration (1996-2012) and validation (1981-1995; 2012-2014) gave varied results, ranging from very good to acceptable, for the monthly flow, sediment load, total nitrogen (TN) and total phosphorus (TP). Various BMPs were implemented into the calibrated model in conjunction with climate data from four Coupled Model Intercomparison Project Phase 5(CMIP5) projections to estimate the effects of climate change on watershed yields. Implemented BMPs include riparian buffers, vegetated filter strips, terrace, grassed waterway, and tillage. BMPs were implemented in subwatersheds with high sediment and nutrient outputs as well as relatively high ecological value. Results indicate that BMPs could achieve reductions in a range from 2 to 76% for sediment loss, 1 to 64% for TN loss, and 5 to 54% for TP loss. Among the individual BMPs assessed, vegetated filter strips were most effective when considering the reduction in sediment and nutrient loads. This study highlights the effectiveness of a range of BMPs in reducing the sediment and nutrient loads and provides quantitative measures for determining the most effective individual BMP and the optimal combination of BMPs based on current and future climate scenarios.

Northern Mexico is characterized by semiarid climate, where limited precipitation restricts crop production. Therefore, production needs efficient irrigation techniques. Agriculture is a high water consumption activity that in order to become more sustainable, demands knowledge of irrigation efficiencies at the regional scale. In this work we present a method to estimate irrigation efficiencies of croplands based on the crops’ water demand obtained with remote sensing data. The study site is located in an agricultural valley in northern Mexico in an area of cornfields with approximately 47,000 ha under irrigation that lies on top of an overexploited aquifer with a sustained declining water table (averaging 2m/year of drawdown). Using Landsat 8 derived imagery from the METRIC-EEFLUX site for the period between May 16 (emergence season) to Nov 24 (harvest season) during the 2017 agricultural cycle, the Crop coefficient (Kc), the reference Evapotranspiration (ET0) and the actual Evapotranspiration (Eta) were obtained. To obtain daily values of ETa in between the 16 day Landsat images interval, we used Kc obtained from its relation with a normalization of the enhanced vegetation index (EVI), and ET0 calculated using Penmann-Montheit fed with meteorological data from weather stations in the vicinity of the area. Irrigation efficiency was calculated using the quotient between ETa and water applied during the agricultural cycle measured with volumetric gauges at the outlet of the irrigation systems. For this study we monitored 5 parcels of corn with different irrigation systems: two parcels with drip irrigation, one with sprinkler irrigation and two with furrow irrigation. Results from the irrigation efficiencies ranged from 47 % to 71 % and depend on factors like parcel dimensions, irrigation schemes, scheduling and its spatial uniformity. Crop yield data is used to discuss the impacts of these factors on the irrigation efficiencies and the implications of different water use strategies for the sustainability of water resource-compromised regions. This methodology can be replicated in extensive areas requiring knowledge of irrigation efficiencies for more sustainable water management.

The main objective of the NASA-NSF SWQU “A New-generation Software to Improve the Accuracy of Space Weather Predictions” effort is to develop a data-driven time-dependent model of the solar corona and heliosphere. This model will provide coronal and solar wind predictions and be made available to the public. One key component of this model is the use of a data-assimilation flux transport model to generate an ensemble of synchronic radial magnetic field maps to use as boundary conditions for the coronal field model. While flux transport models have long been established in the community, they are not open source or available for public use. We therefore are developing a new Open-source Flux Transport (OFT) software suite. The computational core of the OFT is the High-Performance Flux Transport code (HipFT). HipFT implements advection, diffusion, and data assimilation for the solar surface on a logically rectangular non-uniform spherical grid. It is written in Fortran and parallelized for use with multi-core CPUs and GPUs using a combination of OpenACC/MP directives and Fortran’s standard parallel ‘do concurrent’. To alleviate the strict time-step stability criteria for the diffusion equation, we use a Legendre polynomial extended stability Runge-Kutta super time-stepping algorithm (RKL2). The code is designed to be modular, incorporating various differential rotation, meridianal flow, super granular convective flow, and data assimilation models. Multiple realizations of the evolving flux will be computed in parallel using MPI in order to produce an ensemble of model outputs for uncertainty quantification. Here, we describe the initial implementation of the HipFT code and demonstrate its validation and performance. We use an analytic solution of surface diffusion and rigid rotational longitudinal velocity to validate the advection and diffusion implementations. We also compare realistic flux transport test problems against the established AFT flux transport code.

Elevated organic matter (OM) concentrations are found in hadal surface sediments relative to the surrounding abyssal seabed. However, the origin of the biological material remains elusive. Here, we report the composition and distribution of cellular membrane intact polar lipids (IPLs) extracted from surface sediments around the deepest points of the Atacama Trench and adjacent bathyal margin to assess and constrain the sources of labile OM in the hadal seabed. Multiscale bootstrap resampling of IPLs’ structural diversity and abundance indicates distinct lipid signatures in the sediments of the Atacama Trench that are more closely related to those found in bathyal sediments than to those previously reported for the upper ocean water column in the region. While the overall number of unique IPL structures in hadal sediments is limited and they contribute a small fraction of the total IPL pool, they include a high contribution of phospholipids with mono- and di-unsaturated fatty acids that are not associated with photoautotrophic sources. The diversity of IPLs in hadal sediments of the Atacama Trench suggests the presence of in situ microbial production and biomass that resembles traits of physiological adaptation to high pressure and low temperature, and/or the transport of labile OM from shallower sediment. We argue that the export of the most labile lipid component of the OM pool from the euphotic zone and the overlying oxygen minimum zone into the hadal sediments is neglectable. Our results contribute to the understanding of the mechanisms that control the delivery of labile OM to this extreme deep-sea ecosystem. Furthermore, they provide insights into some potential physiological adaptation of the in situ microbial community to high pressure and low temperature through lipid remodeling.

Boreal forest heights are closely associated with the global carbon and energy budget. Existing investigations of boreal forests were mainly carried out at plot scales, which cannot be guaranteed on an annual and regional-scale basis given their sampling schemes. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA’s Ice, Cloud and Land Elevation Satellite (ICESat-2) enables the measurement of forest vertical structure at a global scale. However, with a photon-counting system, ICESat-2 receives substantially reduced signals over vegetated regions (low albedo), making its applications in forest height mapping challenging. This study made the first attempt to develop a 30-m canopy height model (CHM) for a mountainous forested site (located at the north of Fairbanks, Alaska) by coupling the ICESat-2 observed canopy heights, Hcanopy (response), with Landsat-8 (L8), Sentinel-1 (S1) and Sentinel-2 (S2) data using a random forest regressor. Here, Hcanopy corresponds to the 95th percentile (RH95) of all identified canopy photons within a 100-m segment. Before CHM development, low-quality ICESat-2 tracks were filtered out by comparing with the reference airborne lidar considering factors such as slope, canopy cover, signal-to-noise ratio, and canopy height uncertainty. Results suggest that: 1) ICESat-2 Hcanopy has the highest correlation with airborne lidar RH95 under strong beams; 2) the errors of ICESat-2 tracks become larger under lower signal-to-noise ratios (<5), steeper terrain (slope >20˚), greater canopy height uncertainty (>0.3) and sparser canopy cover condition (<20%); 3) by adopting the aforementioned criteria in filtering the ICESat-2 tracks, the Pearson’s correlation coefficient (R) between ICESat-2 Hcanopy and airborne lidar RH95 has been significantly improved to >0.8 under any beam strength; 4) based on previous results, we find that incorporating features derived from L8, S1 and S2 produces the most desirable CHM (R=0.85), and S2 overall shows a better capability than L8 in predicting regional-scale canopy heights; 5) among all input features, normalized difference vegetation index (NDVI) calculated based on the first red edge band (703.9nm) of S2 is the leading feature on CHM development, whereas land cover appears the least important.

Coastal wetlands are intertidal ecosystems based on a delicate balance between hydrodynamic, morphological, and biological processes. Increasing rates of relative sea-level rise, sediment starvation and anthropogenic pressure challenge the existence of wetlands and the ecosystem services they support, extending to water quality enhancement, carbon sequestration, and shoreline protection. Therefore, to preserve coastal wetlands and their ecosystem services, it is of utmost importance to understand sedimentation processes that drive salt-marsh vertical accretion and offset the effects of relative sea-level rise. Tidal flooding propagating via the channel and creek system is considered to be the main mechanism controlling marsh sediment supply. However, storm-induced resuspension associated with enhanced water level can importantly affect the marsh sediment budget, sustaining sedimentation on the marsh surface and signing its topography, which, in turn, affects transport processes. To better understand how tides and storm surges affect spatial and temporal sedimentation patterns in salt marshes, we investigated short-term sedimentation processes through field observation in the salt marshes of the Venice Lagoon, Italy. Sediment accumulation measurements carried out continuously from October 2018 to July 2021 in four different marshes reveal that storm-driven sediment supply accounts on average for 70% of the total yearly sedimentation, despite the brief duration of storm events. On marshes bordering channels, sediment mostly accumulates close to the marsh margin and sedimentation rapidly decreases with the distance from the marsh edge, contributing to form a levee-shaped profile. Conversely, on marshes facing tidal flats, where the action of wind waves is stronger, maximum sedimentation shows an inland displacement, creating a gently sloped, ramped transition at the marsh margin. We conclude that storm surges importantly support marsh sediment accumulation and change the spatial depositional patterns, which largely define the marsh topographic profile.

The rate of increase in atmospheric methane (CH4) has accelerated in recent years, reaching 15 ppb/yr in 2020, with causes that are not well understood. Given methane’s potent global warming potential (85x that of CO2 on a 20-year timescale), this indicates a crucial need to better understand its current budget. Near-global high-precision methane column observations from the TROPOMI satellite sensor offer a major advance for mapping methane fluxes. Here we combine two years of TROPOMI data with the GEOS-Chem adjoint model in a 4D-Var framework to optimize global methane emissions at high spatial resolution. The inversions converge on distinct sets of solutions depending on whether methane loss rates are also simultaneously optimized or not. Findings thus show that even with the dense TROPOMI coverage, methane budget inferences remain sensitive to the prior assumptions for OH. The ensemble of solutions adheres to a close linear relationship between the derived global source and sink terms, with each distinct result successfully improving the simulation of globally-available in-situ data. Solutions with methane loss rates treated as a hard constraint exhibit the best consistency with remote OH and CO measurements and with the background seasonal cycle in methane. We further employ multiple inversion formalisms to test the solution sensitivity to the assumed prior emissions. This presentation will explore the derived emission adjustments in terms of their implications for methane flux drivers and potential missing sources.

We report observations of a relatively long period of 3He-rich solar energetic particles (SEPs) measured by Solar Orbiter. The period consists of several well-resolved ion injections. The high-resolution STEREO-A imaging observations reveal that the injections coincide with extreme ultraviolet jets and brightenings near the east limb, not far from the nominal magnetic connection of Solar Orbiter. The jets originated in two adjacent, large, and complex active regions, as observed by the Solar Dynamics Observatory when the regions rotated into the Earth’s view. It appears that the sustained ion injections were related to the complex configuration of the sunspot group and the long period of 3He-rich SEPs to the longitudinal extent covered by the group during the analyzed time period.

Abiotic efflux of CO2 from soil is often attributed to dissolution of carbonates, and therefore not expected to occur in soils with a low pH. However, another abiotic source of CO2, less constrained by pH, may arise from reactions that oxidize natural soil organic matter and reduce metal oxides. Studies of redox reactions between phenolic compounds and Fe and Mn oxides in soil have been focused mainly on the environmental fate of both oxidants and reductants and formation of organic matter. We measured CO2 formed during 3-hour, room temperature (22±2 oC), incubations of samples of archived soils and from an ongoing crop diversity study. Subsamples (8 g. ODE) of each soil, were treated (5 ml) with water, or solutions of glucose (0.029 M), or gallic acid (0.025 M). For each soil, subsamples amended with H2O or with the glucose solution produced little CO2 and were nearly identical to each other, while CO2 quickly formed after treatment with gallic acid regardless of pH. The net increase in CO2 due to gallic acid, observed from the 18 archived soils, ranged from less than 0.5 to more than 80 mg CO2-C kg-1 soil. Significant treatment effects were observed in samples from the crop diversity study with more (Tukey’s P≤0.05) net CO2 from a small grain-fallow treatment compared a 5-year rotation treatment, 19.04 and 15.77 mg CO2-C kg-1 soil, respectively. This study suggests abiotic reactions capable of rapidly producing a burst of CO2 can occur in a wide range of soils following inputs of simple phenolic compounds and be impacted by management regimes. We suggest these are redox reactions in soil linked to Mn or Fe metal oxides and when considered together with fluctuations of carbon inputs to soil and redox cycling, might be a larger contributor to C emissions than previously accounted for.

The Patagonian Icefields (Northern Patagonia Icefield and Southern Patagonia Icefield) are the largest ice bodies in the Southern Hemisphere outside Antarctica. Nonetheless, little is known about their main meteorological and glaciological features (mean state, variability and trends) in present climate (last ~30 years). The lack of temporarily and spatially dense observational data in this area has imposed a limitation on the assessment of the atmosphere-cryosphere interaction, a key issue for understanding the past, present and future evolution of these ice bodies and more generally, the southern Andes cryosphere. In this work, we overcome the absence of surface data by modeling the present-day atmospheric surface conditions for southern Andes. We first use a regional climate model (RegCMv4) to dynamically downscale the ERA-Interim reanalysis at 10-km spatial resolution for the period 1980-2015. Then, we statistically downscale its outputs to a 450-m resolution grid. This meteorological forcing is used later as an input for a simplified surface mass balance model. The surface mass balance output is analyzed particularly for spatial and temporal variability as well as trends. This allows us to have a better understanding of the local-scale atmospheric control (i.e., surface temperature, solar radiation and precipitation) over the surface mass balance of Patagonian Icefields. In order to assess the large-scale control over the surface mass balance of Patagonian Icefields, time series of spatially-averaged modeled fields are projected upon main atmospheric fields obtained from ERA-Interim reanalysis. Main results show that years of relatively high (low) surface mass balance are associated with low (high) pressure anomalies near the Bellingshausen Sea, causing an anomalous cyclonic (anticyclonic) circulation that enhances (reduces) the westerlies impinging the Patagonian Icefields which in turn increments (decrements) the surface mass balance. Only weak correlations between the mass balance and the main atmospheric modes of variability (ENSO, PDO, SAM) were found, suggesting little dependency between these modes and the surface mass balance of the Patagonian Icefields.

The explosion of the Deepwater Horizon (DWH) oil platform in April 2010 on the Gulf of Mexico (GOM) released more than 700 million liters of crude oil. This catastrophe is the largest oil spill in US history and one of the largest in the world that resulted in over 1000 km of oiled shoreline across four different states (FL, AL, MS, and LA). When the floating emulsified oil approached sandy beaches, a portion of the emulsified oil interacted with suspended sediments and sank, forming submerged oil mats (tarmats). Since their formation, hydrodynamic processes fragmented the tarmats to form tarballs. The tarballs are highly mobile in the nearshore environment and can be found on GOM beaches to date. The objective of this proposal is to complete a comprehensive long-term chemical characterization study to investigate the fate of petroleum biomarker compounds used for chemical-fingerprinting of the tarballs collected at different times over the past 10 years along the Alabama shoreline. Petroleum biomarkers are geochemical organic compounds present in crude oils that can be related to their unique biological precursors. Currently, the most common method used for identifying and fingerprinting the oil spill source is characterizing these biomarkers. Hence, the long-term fate of three groups of biomarker compounds (terpanes, steranes, and triaromatic steranes) present in the tarballs collected from the Alabama shoreline is investigated for using them for fingerprinting purposes. There have been contradicting literature data on the degradation levels of these biomarkers, and this field-scale characterization study demonstrates that most of the investigated biomarkers remain recalcitrant after 10 years of natural weathering. We also quantified the changes in the concentration levels of toxic polycyclic aromatic hydrocarbons (PAHs) present in these tarballs, and the results show that many of these PAHs are recalcitrant. These results are consistent with previous studies that indicated that certain heavy toxic and carcinogenic PAHs, such as chrysene, can be recalcitrant and remain in the tarballs for a long time. Therefore, it is important to track the levels of these recalcitrant PAHs in the tarballs to determine their potential toxicity effects.

The Indian subcontinent is one of the hotspots of deadly heat stress. Several attribution studies have shown increasing trends in heatwaves and its linkage with dry spells over South Asia. However, very few studies have investigated concurrent or successive (lagged d-day) occurrence of humid heat stress (high temperature compounded by humidity) and precipitation extremes within a short time window. Using gauge-based observation records of the last five decades, we have analyzed the concurrence of extreme wet bulb temperature, Tw and peak rain events in 9 urban locations of India, distributed over climatologically heterogeneous regions. We find a larger fraction of the population is exposed to a significant increase (more than 1% and up to 2.5%/decade) in mean and extreme Tw (around 1%/decade) in several sites than solely accounting dry-bulb temperature trends. This prompted us to analyze the compound hazard associated with storm events preceded by extreme Tw (assessed through ≥ 95 – 98.5th percentile exceedances and annual maxima series) up to a week of occurrence of the event. Considering synchronicity between two drivers (extreme Tw and peak rain), we demonstrate cities located across the western half of the country showed positive dependence, whereas those located over the eastern half show negative dependence. While negative correlation suggests the concurrence of dry and hot episodes, the positive correlation suggests robust amplification in precipitation extremes. This is confirmed by the large upper tail distributions of peak rain events during the core monsoon season (June – September) to locations showing positive dependence. Based on extreme Tw-precipitation sensitivity, we propose compound heat stress – rain-induced flood hazard model for densely populated areas. Understanding drivers of peak runoff responses would benefit risk management, insurance, and flash flood forecast, devising flood resilience under climate change.

Strong (M 6 - 7) to large (M 7 – 8) earthquakes are capable to produce surface faulting both along the primary fault and distributed faults. The availability of modern technologies clearly highlighted the complexity of surface faulting associated with recent earthquakes worldwide, providing a dataset with unprecedented detail. Surface faulting and deformation pose a threat for critical facilities, lifelines and infrastructures; the assessment of the probability of occurrence of fault displacement is thus vital for risk mitigation and a proper planning. The recent datasets were not yet analyzed in this perspective, and current methodologies and scaling relations rely on data acquired few to tens of years ago. We perform a probabilistic fault displacement hazard analysis on distributed faulting due to modern earthquakes with normal and strike-slip kinematics. We show that current scaling relations tend to underpredict the actual occurrence of faulting, and we propose updated relations. Distributed faulting due to a large earthquake on one hand, and repeated ruptures at the same spot in a short time interval (e.g., Central Italy, 2016; Searles Valley and Ridgecrest 2019) on the other hand, are the end-members of a spectrum of surface faulting behavior. If not taken into account during the interpretation of paleoseismological data, different modes of fault rupture can significantly bias the derived earthquake parameters and, consequently, inferences on the regional tectonic activity.

Solar X-ray flares often drive coronal mass ejections and solar energetic particles (SEPs), which are key elements of space weather near the Earth and beyond. Machine learning with capability to improve space weather forecasting, prevents the adverse consequences of the space weather phenomena. We study temporal parameters of the solar X-ray flares associated with the SEP events. The temporal profile of the X-ray flare is usually divided into rising and decaying phases each approximated by a single functional representation. We observe a more complex nature of the rising and decaying phase for number of X-ray flares, which includes a pre-flare increase of intensity and a break in the decay phase. We develop a method to define, derive, and provide an optimized set of X-ray flare temporal profile parameters which takes into account the complex nature of the flare. We create a set of X-ray flare temporal properties which we next apply to the machine learning algorithms for a better forecasting of X-ray flare evolution in time and the associated SEP events.