Landslides are one of the most devastating natural hazards. A landslide inventory contains the location, date, and extent of landslides. Landslide inventory is crucial to understand landslides, simulate landslides and develop early warning systems. Historically the landslide inventories have been prepared by archive studies, interviews, and geological fieldworks. Although precise, these methods, are costly and only useful for places that are accessible by transportation. The alternative is remote sensing-based landslide identification as a cost-effective method to identify landslides in remote places. In this study, we use a combination of satellite rainfall data, optical satellite data, SAR (Synthetic Aperture Radar) data, and landslide susceptibility data to identify and validate the location and extent of historical landslides. Firstly, we reduce the computation required by using Landslide Susceptibility Map to identify areas that are susceptible to landslides. Next, we identify the onset of rainfall from satellite rainfall data and subset the SAR and optical images for the spatiotemporal scale determined in the earlier steps. The pre-and post-landslide images show high contrast between landslide and non-landslide areas. The proposed algorithm merges the pre- and post-landslide images to create a data vector, and then uses machine learning to segment the merged data vector into landslide and non-landslide parts thus identifying the landslide location and extent. The training data for the supervised machine learning methods are developed from earlier well-identified landslides. The efficacy of the model is tested on landslides previously unseen by the model using metrics like IOU. All the above frameworks are developed using google earth engine python API to leverage googles' cloud infrastructure for data access and computing. The methodology has been developed with an application in India but can be applied globally.

The lowest temperature in Earth�s atmosphere is found at the mesopause, above which the temperature increases monotonically in the thermosphere. Here we use the measurement of the MIGHTI instrument onboard NASA�s ICON satellite to characterize the neutral temperature variations in Earth�s low-latitude mesosphere and lower thermosphere during Northern Hemisphere solstice. We compare ICON temperatures with empirical models and study the discrepancies between the model and the observations. Finally, ICON temperatures are compared with coincident measurements of TIMED/SABER observations.

Rare earth elements (REEs) are essential parts of green energy technologies and electronic devices; hence they are vital for preserving the quality of life. To meet the present and future demand of REEs, it is critical to understand one of the major sources, the REE carbonate deposits. In this study, we explored the crystallization kinetics of REE-carbonates during the interaction of REE-rich (La, Pr, Nd, Dy) aqueous solutions and calcite (CaCO3), dolomite (CaMg(CO3)2) or aragonite (CaCO3) at low hydrothermal conditions (25-220 �C). The experiments resulted in the solvent-mediated surface precipitation and subsequent pseudomorphic mineral replacement of the calcite/dolomite/aragonite seeds by newly formed rare earth carbonates. The replacement occurs from periphery inwards. The newly formed REE-bearing carbonates follow the crystallization sequence: lanthanite [REE2(CO3) 3�8H2O] ? kozoite [orthorhombic REECO3(OH)] ? hydroxylbastnasite [hexagonal REECO3 (OH)]. An exception under this is of the Dy-doped experiments; the interaction of Dy-bearing solutions with calcite/dolomite resulted only in the crystallization of kozoite [orthorhombic DyCO3(OH)]. However, experiments with aragonite revealed a two-step crystallization pathway: tengerite [Dy2(CO3) 3�2-3(H2O)] ? kozoite [orthorhombic DyCO3 (OH)]. La- and Nd-kozoite, grows oriented onto the calcite surface, forming an epitaxy, due to their structural similarities. The temperature, the solubility of the host mineral and the ionic radii of the REE3+ in question were found to control the kinetics of the replacement reaction, the polymorph selection, and the crystallization pathways towards bastnasite. These findings allow us to gain a more in-depth understanding of the formation REE-bearing carbonates, especially the mineral bastnasite, which is the main source of REEs for industry. The results provide key information to understand the behavior of REEs during fluid-rock interaction in REE-carbonate deposits. This knowledge can be used to improve REE separation, exploration, exploitation and develop new recycling methods, as well to produce carbonate minerals with tailored structure and chemistries.

Soil and its water content can remain unfrozen below an insulative snow cover and modulate snowmelt infiltration and runoff. In this paper, a forward emission model is proposed to account for L-band microwave emission of wet soils below a dry snowpack covered with an emerging moderately dense vegetation canopy. The model links the well-known tau-omega emission model with the snowpack dense media radiative transfer theory as well as a multi-layer composite reflection model to account for the impacts of a snow layer on the upwelling soil and the downwelling vegetation emission, respectively. It is demonstrated that even though dry snow is a low-loss medium in L-band, omission of its presence leads to underestimation of soil moisture (SM), especially when soil (snow) becomes wetter (denser). Constrained inversion of the forward model, using brightness temperature observations from the Soil Moisture Active and Passive (SMAP) satellite, shows that the retrievals of SM and vegetation optical depth (VOD) are achievable with unbiased root mean squared errors of 0.060 cm3.cm-3and 0.13 [-], when compared with in situ data from the International Soil Moisture Network (ISMN) as well as VOD-derived values from the Normalized Difference Vegetation Index (NDVI) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations.

It is vital to have solid understanding of responses of regional climate change to many external drivers. The contributions of greenhouse gasses, anthropogenic aerosols, and natural forcings to declining trend in late summer precipitation over East Asia transitional climatic zone (TCZ) were evaluated with CMIP6 models., Late summer precipitation across the TCZ has decreased significantly between the period of 1951 and 2013. The decreasing trend as agreed with gridded dataeset. CESM2, ACCESS-ESM1-5, GFDL-ESM4, and HadGEM2-GC31 out of the 13 models could capture the observed declining trend. Our results show that the decrease in precipitation across the TCZ is mostly attributable to the influence of the aerosol forcing. With a relative contribution of 139%, these suggest that human-made aerosols may have slowed the circulation of the East Asian monsoon, which may have caused the TCZ to dry out. The dominating EOF of observed precipitation accounts for roughly 30.3%, while the second EOF accounts for 13.5% of the total variance, with somewhat higher magnitude in the front edge and low magnitude in the rear edges. The links between the multi-model ensemble (MME) leading mode and precipitation variability are reproduced. However, persistent bias in simulated late-summer precipitation variability associated with the inherent uncertainty in coupled models still exists when compared to observation this work offer a scientific basis for vulnerability and climate risk assessments across the East Asian transition climactic zone.

Geochemical reactions, like mineral dissolution or precipitation, may significantly alter the rock matrix by dissolving or precipitating minerals at the fluid-mineral interfaces. These may lead to changes in porosity and permeability in varied time scales. Conventionally, natural rocks are used to understand these complex changes among pore spaces. However, predicting these physical alterations and the impact on hydrologic properties is challenging because of rock-mineral heterogeneity and the inability to repeat lab experiments with controlled variations in parameters. Thus, 3D printed reactive porous media can be an alternative way to determine porosity and permeability evolution due to mineral dissolution and precipitation reactions by representing and replicating natural rock heterogeneities. To print 3D printed porous media, 3D X-ray Computed Tomography (X-ray CT) images of Bentheimer sandstone are captured, segmented, and meshed. Samples are magnified and printed using fused filament fabrication 3D printing. X-ray CT images of printed samples are collected and analyzed to compare petrophysical properties with real rock samples. Then, core-flooding experiments are done using these 3D printed samples to observe the impacts of dissolution and precipitation reactions on porosity and permeability evolution in replica experiments with controlled parameter variation. While measuring pressure differences across the sample helps to determine permeability, temporal 3D X-ray CT images can reveal the evolution of reactions and the pore structure. Eventually, these results will help to learn more about porosity and permeability evolution from mineral dissolution and precipitation reactions in heterogeneous porous media.

The strength of foliated rocks within the crust and upper mantle may be dependent on the orientation of the foliation relative to the direction of maximum compression. Recent axial compression experiments on gneisses deformed by crystal plastic mechanisms indicate that foliation orientation does not significantly affect the strength. However, this lack of strength anisotropy may be transient and increase as strain increases due to processes that modify the textures in these rocks such as dynamic recrystallization and/or metamorphic reactions. To investigate the evolution of strength anisotropy and deformation mechanisms in foliated rocks as a function of initial foliation orientation, we performed general shear deformation experiments on slices of cores of a fine-grained gneiss with three different foliation orientations. The fine-grained gneiss (grain size ~50 ?m) is composed of 43% quartz, 40% plagioclase, 16% biotite, and 1% accessory minerals. Slices of cores with foliation orientation perpendicular, parallel and at 45� to the compression direction were deformed in a general shear geometry at a temperature of 800�C, confining pressure of 1.5 GPa, and strain rate of 6*10-5/s to a shear strain of 2. The peak strength (1200 MPa) of the shear slice with foliation perpendicular to the compression direction was greater than the peak strengths (1000 MPa) of shear slices with the foliation oriented parallel and 45� to the compression direction. However, the magnitude of strain weakening (1000 MPa) of the shear slice with foliation perpendicular to the compression direction was greater than of the magnitude of strain weakening (500 MPa) of the two other orientations. Microstructures in quartz and plagioclase include undulatory extinction and bulging recrystallization indicating deformation by dislocation creep. Biotite grains are sheared and rotated parallel to the shear plane. These results indicate that strain weakening may increase as the degree of textural evolution necessary to rotate grains parallel to the shear plane increases.

The dominant dissipation mechanism at play in turbulent collisionless magnetized plasmas remains an open question. Kinetic Alfv�n Waves (KAWs) are often believed to be a primary candidate as they are theoretically able to dissipate energy at electron scales; however, direct measurements of these smallest scales have been historically difficult to achieve. Recent observations of the near-sun (~0.074 au / ~15.9 Solar Radii ) solar wind during Parker Solar Probe�s (PSP) Encounter 9 provide direct measurement of electric and magnetic fields in the electron dissipation range. Wave behavior is characterized by comparing measurements of the ratio of the wave electric to magnetic fields as a function of frequency to existing theoretical predictions for KAWs. Deviations from theory near proton scales are observed, along with decoherence of the electric and magnetic fields, before returning to coherent KAW behavior beyond ion scales. Explanations for these deviations are sought by a several means. First, we compare observations to numerical solutions from the PLUME dispersion solver for the case of a hot, drifting, electron-proton plasma with Bi-Maxwellian velocity distributions. Secondly, simultaneous observations of the Helicity Barrier in the electric and magnetic power spectra are considered as an explanation for deviations from KAW theory near proton scales.

Healthy soils serve as a large carbon sink and act as a major buffer against climate change. Microbes may adapt or acclimate to ecosystems in response to warming due to climate change. Adaptation of microbial traits has been observed due to warming, which irreversibly changes microbes' behavior in ecosystems. However, it remains unknown what microbial traits are associated with warming and whether they are adaptive. We hypothesized that isolates from heated soils have adapted (1) lower temperature sensitivities of growth; (2) higher optimum growth temperatures; and (3) higher maximum growth temperatures in comparison to isolates from control soils. We studied bacterial isolates from a long-term warming experiment at the Harvard Forest where soils were heated 5�C above ambient temperatures throughout the year. We measured bacterial growth (optical density at 600 nm) in liquid media for 24 isolates of Alphaproteobacteria over time spanning temperatures from 20-37�C along 2-3�C increments. Model fitting was used to calculate growth rate, temperature sensitivity of growth, optimum growth temperature, and maximum growth temperature. We used phylogenetic comparative methods to determine whether microbial growth traits were adaptive. Data for temperature sensitivity of growth, optimum growth temperature, and maximum growth temperature indicate that there is not a significant difference between these traits for isolates from heated plots versus isolates from control plots. These results suggest that bacteria from heated plots have not adapted lower temperature sensitivities of growth, higher optimum growth temperatures, or higher maximum growth temperatures than bacteria from control plots. Our results may be explained by the media being too nutrient rich, where growth under carbon limiting conditions may reveal adaptive growth traits. As temperatures increase, changes in soil microbial growth rate may affect rates of atmospheric carbon cycling. Future exploration of whether growth strategies explain microbial adaptation to warming will help predict changes in microbial community and ecosystem function and allow us to better understand soil microbial responses to warming.

Electricity grids throughout the world face the dual challenge of decarbonizing while also withstanding increasing severe weather due to climate change. Grid operators must manage the integration of variable renewable energy sources and multi-sector electrification (i.e., increased demand) while also preparing for more frequent and severe droughts, heatwaves and cold snaps, which can adversely impact grid reliability. Consequently, simulation models of power systems, scalable for multiple hydrometeorological stresses, are critically needed to manage risks while reaching net zero emission goals. A persistent challenge for power system modelers has been the need to achieve adequate model spatiotemporal fidelity (e.g., time resolution and spatial scale) and computational cost (run time). This problem has been made more difficult to solve due to insufficient open-source data, often leading to oversimplification of necessary system components, which can bias the assessment of power system operations during extreme events. In this study, we are introducing a solution to this problem in the form of an open-source software that allows users to easily customize the temporal resolution with the desired spatial scale and track the accuracy of grid simulation models. We demonstrate our scalable approach for the U.S. Western Interconnection using existing synthetic grid datasets maintained by Texas A&M University. Our approach allows users to search over a wide range of model parameters (e.g., mathematical formulation, network topology, transmission line scalars, and economic hurdle rates) to find a model instantiation that accommodates their experimental needs. In this study, we demonstrate how this scalable approach supports the user in finding the best version of the model to examine specific natural hazards (e.g., heat waves, droughts, etc.). The grid models we developed are easily interoperable with observed balancing authority level data, as well as a range of multi-sector modeling platforms.

The geochemistry of coral skeletons is widely used in tropical paleoclimate reconstructions. However, the quality of the reconstruction can be largely influenced by diagenesis, a form of fine-scale chemical and structural alterations to the aragonite skeleton due to partial dissolution or infilling of secondary aragonite or calcite. We analyzed the impact of intra-skeletal calcite diagenesis on multiple coral geochemical proxies (?18O, Sr/Ca, Mg/Ca, Li/Mg, Li/Ca, U/Ca, B/Ca, Ba/Ca, and Mn/Ca) in a Porites sp. fossil coral from the Marshall Islands. The level of preservation was examined using a scanning electron microscope and quantified using X-ray diffraction (XRD). Each micro-milled coral sample was split into two aliquots for geochemistry and XRD analysis to quantify the direct impact of calcite on geochemistry. We modified the traditional non-destructive XRD method used for powder samples to detect low levels of diagenesis (1�2%) in low-mass samples (~3�10 mg) that are not visible in X-radiograph images. A laboratory-prepared standard with known amounts of inorganic calcite (2%) and pristine coral aragonite (98%) demonstrates the accuracy and reproducibility of our method (SD = 0.33%, 2?). All geochemical tracers respond at different levels to the presence of calcite. Sea-surface temperature (SST) proxies such as Sr/Ca, Li/Mg, Li/Ca, and ?18O decrease with increasing calcite content. Such an impact can reach up to 0.5 ?C per 1% calcite when converted to temperature, assuming a Sr/Ca-SST calibration of 0.05 mmol/mol ?C-1. While site-specific calibrations are ongoing, these results imply 2.8% calcite would produce a Sr/Ca-SST signal that is on par with local SST seasonality (~1.5 ?C); therefore, this could serve as the cut-off level for screening calcite diagenesis. Proxies used to reconstruct the carbonate chemistry of the semi-isolated calcifying fluid are also sensitive to calcite diagenesis, with lower B/Ca and U/Ca and higher Mg/Ca. Therefore, we recognize the importance of quantifying trace levels of calcite diagenesis, especially in sub-aerial fossil corals used for reconstructing past climate variability.

Proterozoic Newania carbonatite complex in western India is a plutonic dolomite-carbonatite occurrence with minor intrusions of ankerite-carbonatite. The complex is related to Aravalli rift and intrusive in Untala granite of Banded Gneissic Complex (BGC-I). Sodic to potassic fenitized aureole surrounds the carbonatitic intrusion. Mineralogical study of dolomite-carbonatite is carried out which revealed that the common phosphate minerals in carbonatites i.e. apatite and monazite, can act as recorders of magmatic and post-magmatic stages of carbonatite evolution Apatite is a ubiquitous mineral in the Newania carbonatite complex which displays variable intraintrusion modal abundance and textural relationships. Two generations of apatites namely, Ap1 and Ap2 are recognized. Ap1 apatites are early crystallized, fine-grained subhedral cumulate crystals confined to few mm thick layers. Ap2 apatites are crystallized later to Ap1 apatites as fine-grained subhedral to anhedral crystals, commonly having an oval shape. These occur as disseminated grains and loose chains. Edges of apatites in contact with carbonate phases in both generations are smoothened which is a typical characteristic of apatites in plutonic carbonatites and a consequence of crystal erosion due to ambient magma. Compositionally, Ap1 apatites are Sr-Na-LREE-poor fluorapatites and Ap2 apatites are Sr-Na-LREE-rich fluorapatites. These exhibit a trend of decreasing Ca with increasing Sr, which is similar to the apatites present in other Indian carbonatites. The textural and compositional characteristics of Newania apatites suggest their magmatic origin from parent carbonatitic magma which became progressively enriched in Sr, Na and LREEs with evolution. Monazites in the Newania carbonatite complex are always associated with apatites as fine- to very fine-grained subhedral to anhedral crystals. Two textural varieties of monazites, Mnz1 and Mnz2 are identified. Mnz1 monazites are associated with Ap1 apatites, occurring as compact clusters and very-fine inclusions in apatites. Mnz2 monazites are discrete crystals, associated with Ap2 apatites. Monazites are either occurring as porous pseudomorphs after apatite or are penetrating into apatite crystals. The porous texture of monazite pseudomorphs is a product of the dissolution-reprecipitation process. The petrographic studies precludes the possibility of magmatic origin of Newania monazites. Compositionally, both Mnz1 and Mnz2 monazites are monazite-(Ce), containing 68.37-70.51 wt. % ?LREE content and fall within the compositional field of carbonatitic monazites. Both varieties of monazite have significant compositional contrast in terms of their La, Pr, Nd and Sm contents but display similar compositional trends as those in other carbonatitic complexes in the world. Textural and compositional characteristics of Newania monazites indicate their origin during the post-magmatic stage of carbonatite evolution. A late-stage fluid is exsolved from carbonatitic magma during late- to post-magmatic stages, characterized by H2O- and K-rich compositions and temperature between 400�C to 900�C that caused partial to complete dissolution of magmatic apatites and precipitation of monazite.

Nitrogen ion abundances are substantial in the topside ionosphere, second only to that of oxygen ions. While N+ and O+ have similar masses, they have different ionization energies and undergo different chemical processes. Previous studies have shown that during moderate geomagnetic activity, N+ density in the inner magnetosphere rivals that of O+, and that ion composition regulates many of the magnetospheric processes. Hence, tracking the differential transport of nitrogen and oxygen ions provides insight into mass flow and energy coupling throughout the ionosphere�magnetosphere system. Combining physics-based multifluid magnetohydrodynamic modeling and observation data of the September 7�8, 2017 storm, we assess the impact of heavy ion composition on the global magnetosphere by examining the total ion pressure, magnetotail reconnection location, and the pathways of N+ and O+ as they are transported from 2.5 RE to the nightside, as well as validation of the simulation results with plasma measurement data from Cluster, THEMIS, etc. Even a small difference in heavy ion composition at the inner boundary of the computational domain (set at 2.5 RE) leads to sizeable changes in the ion pressure on the nightside near-Earth plasma during the main phase of the geomagnetic storm, as shown in the contour in the figure. The location of the magnetotail reconnection site on the Sun�Earth line is further downtail when N+ abundances increase at the inner boundary. In addition, even under this simple assumption, the transport pathways of N+ and O+ diverge, allowing the two ion species to travel through different regions in space and opening up the possibility of experiencing different energization mechanisms.

Groundwater nitrate concentrations in excess of 10 mg/L pose serious health risks to those using the water. Predicting where and when individuals will be exposed to this hazard is important to implementing solutions and protecting communities. We used a machine learning model to create county-level predictions of groundwater nitrate in the continental United States through the year 2100. The predictions are based on 25 Coupled Model Intercomparison Project Phase 6 general circulation models and three shared socioeconomic pathways. Analysis was completed on both climate-consistent regions and individual counties. We also calculated return periods for each county to reach 10 mg/L under all investigated realizations. We found that changes due to climate have a minimal effect on regional trends and a larger effect on individual counties. We identified 27 counties that had contamination in excess of 10mg/L in realizations in all three pathways and an additional county that had excessive contamination in just two. We also determined that increasing severity of climate change tended to cause return periods to grow longer. Findings from this research can be used to find locations most at risk of groundwater nitrate contamination and guide strategies to limit and remediate nitrate contamination.

To protect and restore vital ecosystem services in this era of global change, we must predict how management and conservation practices will impact the trajectory of the ecosystem. In tropical rainforests, this is a complicated task as many different facets are inherently connected and the restoration of one ecosystem service, like biodiversity, often depends on the state of other variables, such as the physical canopy structure of the forest. Here we investigate Empirical Dynamic Modeling (EDM) as a minimally assumptive, but interpretable machine-learning approach to studying change-over-time in ecological data. EDM is an equation-free approach that uses the history of a system�s states to chart the system�s future. In effect, the sequence of change in time can stand in for other variables that haven�t been identified (or perhaps not even measured!). The challenge with EDM is that you often do not have data on all possible states which is especially hard when the system develops slowly, as in a tropical forest. One way this problem has been addressed is by substituting space for time to increase the number of observations. In this study we explore a further method of using spatial data in EDM which leverages the fact that in these plots, the spatial replicates are not independent and that spatial sequences between neighboring points can stand in for temporal sequences. We applied this spatial approach to forecasting change over time in tree community structure at intermediate spatial scales (~60m) using the ForestGEO tree survey at Barro Colorado Island. The 50 ha site was censused 8 times over a 35-year period which allows us to compare the prediction accuracy of spatial EDM methods to the traditional, solely temporal, EDM approach. We found that orienting spatial patterns in neighboring cells by elevation gradients or by growth magnitude both led to better forecasting than using cardinal directions or randomly orienting spatial embeddings, and could achieve comparable predictability to traditional temporal EDM. In addition to potentially unlocking new insights into the spatial processes relevant to management (like edge effects and gap filling), this fully spatiotemporal EDM opens the possibility of applying EDM forecasting and causal analysis to (spatially structured) time series studies as short as just two points.

The wide extension of drylands on Earth's land surface leads to contrasting development, with natural sites and managed ecosystems present within short distances. This spatial variability leads to uncertainties in characterizing and quantifying drylands water and carbon dynamics. Croplands are an example of managed sites with very different vegetation and hydrologic conditions than those naturally prevalent in arid environments. Recent studies have brought to attention that the distinctive water dynamics of croplands can be the source of unaccounted-for carbon dioxide release, contributing to the uncertainty in the dryland's biogeochemical cycles. Pecan orchards are an important cash crop, as the US is the world's largest pecan producer, and two of the leading producing states, New Mexico and Texas, are drylands. As the uncertainties over fresh water supply grow in these regions, the resilience of pecan production is unclear. Yet, studies of vegetation, carbon, and water dynamics are scarce for dryland pecan orchards. This scarcity limits the ability to predict vulnerability in a changing climate or propose improved resource management. To establish a baseline of seasonal variability of vegetation status driven by water availability, we calculated satellite-derived vegetation indices for a pecan orchard in Southwest Texas using Sentinel 2-A products through Google Earth Engine. The indices calculated for this purpose include NDVI, EVI, NIRv, CCCI, and NDII. The values of the different indices were consistently heterogeneous across the orchard parcels, which is attributed to varying percentages of canopy cover, tree health, and age. Nevertheless, a well-defined growing season was identified along with multiple water stress periods. Establishing these vegetation indices allows for monitoring temporal dynamics and seasonal changes in the crop, assessing canopy coverage, tracking seasonal changes in GPP dynamics, and quantifying the canopy water content. This study will establish important baseline features of pecan canopy dynamics for future scaling studies and for integration with UAV-derived vegetation indices and eddy-covariance fluxes to improve our understanding of drylands crop water and carbon dynamics.

The Pacific Northwest margin is a dynamic environment that has experienced large changes since the Last Glacial Maximum (LGM). Proximal and distal glacial retreat of the Cordilleran Ice Sheet (CIS), catastrophic floods, active tectonics, regional volcanism, and variable hydroclimate and productivity all influence sedimentation and post-depositional processes on the margin, and are likely to affect the magnetic record in complex ways. We investigate how these processes influence the paleomagnetic and environmental magnetic record in three co-located sets of piston/trigger cores from the upper continental slope (~825m water depth) just south of Astoria Canyon on the Oregon margin. Preliminary correlations are supported by foraminiferal radiocarbon dates, indicating recovered sediments range in age from the LGM to the modern. We combine u-channel sediment magnetic data with physical properties, computed tomography (CT), x-ray fluorescence (XRF), and other datasets to better understand stratigraphic variations in magnetic mineral assemblages and how these properties reflect paleoenvironmental changes and influence the paleomagnetic properties of the sediments. A distinct transition in lithology and depositional structures dated to ~12ka is evident in physical properties, CT scans, and preliminary geochemistry, likely reflecting the end of ice-proximal depositional processes. Holocene sediments are texturally relatively homogenous muds, with early Holocene lamination preservation likely reflecting margin hypoxia. Late glacial sediments are characterized by greater textural and mineralogical variability, with recurrent silt-clay couplets presenting as mm-scale laminations in the late deglacial, and cm-scale structures in the early deglacial. Magnetic susceptibility is low through the Holocene, and higher during the early deglacial. Inclination trends are replicated between the cores, are comparable to other regional records, and are centered near geocentric axial dipole predictions. Measurements of anhysteretic remanent magnetization (ARM) and kARM/k, in the context of lithologic and XRF data, will assist in determining where diagenesis affects the recorded magnetic signal, changes in source and transport, and changes in environmental processes through the deglaciation.

Asteroid Vesta which is the second largest asteroid in the asteroid belt, and has a differentiated internal structure similar to the terrestrial planets, is considered to be an ideal example of the planetesimal stage of accretion in the solar system. Geochemical and petrological studies of the Howardite-Eucrite-Diogenite (HED) suite of meteorites, that are thought to have originated from Vesta, reveal important information about the accretion and differentiation processes in early planetary bodies. The depletion patterns of moderately siderophile elements in HEDs indicate that core-mantle equilibration in Vesta occurred at high temperatures consistent with a global magma ocean on Vesta. During core-mantle differentiation, highly siderophile elements (HSE: including Re, Os, Ir, Ru, Pt, Rh, Pd and Au) show a strong affinity for molten iron-rich metal and sulphides, and hence can provide important clues about core formation conditions in differentiated planetesimals. This work constrains the depletions of HSEs in the Vestan mantle from abundances reported for diogenites and eucrites, and tests whether these depletions are consistent with the high-temperature metal-silicate partitioning behaviour of HSEs.

Models of radial anisotropy provide essential constraints on strain and the relationship between stress and deformation in the lithosphere and asthenosphere. However, disagreements between current continental-scale models show that a consensus has not yet been reached on variations in radial anisotropy in the upper mantle beneath the US. These models do not use information from earthquake-derived Love wave phase velocities, which can help constrain radial anisotropy. However, isolating the fundamental-mode Love wave from interfering overtones is a significant challenge; it is well established that overtone interference can lead to systematic bias and scatter in Love wave phase velocity measurements. As a result, at the continental scale, there are many fewer studies of Love wave phase velocity than of Rayleigh wave phase velocity. In this study, we present the first earthquake-derived Love wave phase velocity maps for USArray. We show that by selecting Love wave measurements with a large time separation between the fundamental mode and first overtone, we can significantly reduce the detrimental effects of overtone interference, producing more accurate maps. Since the time separation between the fundamental mode and first overtone is governed by path-averaged group velocities, our selection approach shows that regions with many paths through continental lithosphere may have significantly higher-quality Love wave measurements. The differences between our phase velocity maps and predictions from an isotropic model suggest radial anisotropy in the Western US at crustal and lithospheric depths, with the exception of the Colorado Plateau and the Sierra Nevada range, and at short periods, the Snake River Plain. Our maps are consistent with isotropic predictions in much of the central and eastern US at shorter periods, suggesting that radial anisotropy is not ubiquitous in the continental crust. We solve for a 3-D model of radial anisotropy in the uppermost mantle beneath the conterminous US by incorporating additional seismic observables: constraints from receiver functions and Rayleigh wave phase velocity maps. Our work helps understand the impact and variability of overtone interference, offers a simple method to avoid it, and presents a powerful new dataset to study radial anisotropy beneath the US.

Earth System Models (ESMs) exhibit significant biases in runoff sensitivities compared to observational estimates. These biases are an important source of uncertainty in projections of surface water availability, so the model's sensitivity to climate change is open to question. To provide a sound basis for future predictions, an investigation into the mechanisms controlling runoff sensitivity within the land component of ESMs is warranted. Here, we conduct this investigation within the Community Land Model (CLM5), the land component of the Community Earth System Model (CESM). Our study focuses on the Upper Colorado River Basin (UCRB), motivated by the inconsistent model estimates of runoff sensitivity to temperature changes in the basin. Previous analysis often relied on a priori model parameters that are at best first order estimates intended for global simulations. In an effort to improve the representation of dominant processes, we constrained CLM5 parameters against naturalized streamflow and snow measurements in the UCRB. A set of sensitive parameters were identified, many of which are directly related to vegetation and soil evaporation instead of hydrology and snow processes. The optimization of sensitive parameters improved the median Kling Gupta Efficiency value from 0.37 to 0.64 across 20 naturalized streamflow locations in the UCRB for the optimization period (1980-1988). Our study addresses the following questions: Does the model reproduce the 1980 to 2020 discharge deficit? How much have temperature changes contributed to that deficit? What are the mechanistic drivers underlying the influence of temperature on runoff in CLM5? We focus particularly on the role of surface radiation balance and its modulation by the snow-albedo effect and vegetation. The diagnosis we undertake here seeks to reveal structural model uncertainties in runoff sensitivity to support improving ESMs� land model component.

Open burning of Municipal Solid Waste (MSW) is a significant source of GHGs emissions. In both developed and developing countries, MSW mismanagement adversely impacted public health. Most regulating authorities are poorly managing such a large amount of solid waste due to a lack of source segregation of waste, doorstep collection, options for recycling and reuse, technologies for treatment, and disposal methods. These factors play a crucial role in the MSW burning, resulting in elevated emissions. The emission inventories of such sectors critically serve as an input for air quality studies and atmospheric chemistry and climate models. The present work illustrates a national-level emission inventory of MSW burning across India at a finer resolution of 0.1� � 0.1�. The waste generation for 2022 was estimated to be at ~200 million tons in the Indian scenario out of which ~30% is burnt to open. Sixty-five major city emissions have been calculated separately to derive the contribution and emission pattern. The emission data of the Particulate and Gaseous pollutants viz. PM10, PM2.5, NOx, and CO from open burning of MSW have been estimated and spatially allocated in this work. The developed gridded emissions inventory may serve as a critical input to atmospheric chemistry models to quantify its contribution to air quality assessment in various regions of India making a way for policymakers to create better mitigation strategies.

Numerous studies have hinted at the key role of land surface heterogeneities for developing secondary circulations in the atmospheric boundary layer. Observational studies and large eddy simulation (LES) models have been used to understand the role of these circulations in boundary-layer development, cloud processes, and documented biases in measurements such as eddy-covariance energy fluxes. However, these studies have been limited in their ability to capture these events and robustly test hypotheses. In this study, we ask whether diagnosing these circulations and quantifying the transport occurring at their corresponding scales can help us observe all the relevant scales of surface atmospheric transport. We use the LES model PALM to simulate diurnal cycles for four days chosen from late summer (two days) to early autumn (two days) over a large (27x30 km) heterogeneous study domain that was the field site for CHEESEHEAD19, a field campaign designed to intensively sample and scale land surface heterogeneity and the lower atmospheric response across a northern Wisconsin landscape. The simulations were initialized using NOAA - HRRR as large-scale forcing data over the study domain. To investigate surface atmospheric feedbacks, such as self-reinforcement of mesoscale circulations over the heterogeneous domain, the simulations were forced with an interactive land surface model with coupled soil, radiative transfer, and a plant canopy model. Secondary circulations induced due to the surface heterogeneities are diagnosed from the 3D LES data using time and ensemble averaging following Maronga and Raasch (2013). The dynamical nature of the structures are compared between the summer and autumn simulations, showing differing atmospheric boundary layer stabilities. The impact of these secondary circulations on local turbulent fluxes and simulated surface energy balance is also discussed.

We use a Landlab-based numerical simulation to explore the effects of stochastic landsliding on sediment flux from small steady-state catchments. Our goal is to characterize the timescales over which signal shredding occurs. We use the Landlab SPACE component to parameterize bedrock erosion and sediment transport and deposition by fluvial processes, and the BedrockLandslider component to parameterize deep-seated landslide erosion and deposition. Initial runs that provide for only minimal fluvial reworking show that frequent, small landslides cause small variations about a mean sediment flux exiting the catchment, while infrequent, large landslides generate pulses of sediment separated by periods of minimal hillslope erosion. Power spectral analysis of the sediment flux timeseries show a characteristic �signature� of red noise, indicating autocorrelated processes, at timescales shorter than the landslide return time and transitioning to white noise, indicating no correlation in time, at longer timescales. This signature can be split into a fluvial component characterized by red noise, or autocorrelation, at all timescales and a landslide component that generates the transition point due to a finite-size effect occurring at timescales when the majority of hillslopes in the catchment co-fail. Future modeling will explore the range of parameters and model conditions that may affect the sediment flux signal, such as the transport length scale of fluvial sediment and the size and shape of the watershed.

It is well-established that positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land-atmosphere feedback mechanisms, disrupt the global carbon cycle, and accelerate climate change. Permafrost dynamics are relevant to the global community because the distribution of this frozen ground substrate characterizes nearly 23 million square kilometers of the northern latitudes. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impact. Current earth system models do not account for permafrost carbon feedback mechanisms; we are exploring, simulating, and quantifying this limitation with field-scale surveys (i.e., landscape characterization, thermo-erosional features and gullies, active layer depth, and cryostructures) and numerical modeling, image processing, and machine learning at scale across the tundra and boreal ecosystems (TTE). This research seeks to identify, interpret, and explain the causal links and feedback sensitivities attributed to permafrost degradation and terrestrial carbon cycling asymmetry with in situ observations, remote sensing imagery, forest gap modeling (SIBBORK-TTE), multimodal data assimilation architecture, and a hybridized deep learning ensemble of recurrent, convolutionally-layered, memory-based networks (GeoCryoAI). Preliminary metrics obtained from mirroring freeze-thaw dynamics and soil carbon flux across four subdomain in Alaska yield a deep quantile-based mean-absolute error of 0.00131. More specifically, this data-driven modeling ensemble is composed of a convolutional neural network-filtered (CNN) long short-term memory-encoded (LSTM) recurrent neural network that integrates teacher learning from in situ observations while embedding satellite-based measurements and time series datasets into a network of activation functions and processing layers. These outputs are then trained within a variational autoencoder framework (VAE) that encodes and imputes proper decoding protocol necessary for generative adversarial training, benchmarking, and reconstructing synthetic time series data for gap-filling and feature learning. Ongoing work demonstrates the fidelity of monitoring ALT variability as a sensitive, silent-but-pronounced harbinger of change; a unique signal for characterizing and forecasting permafrost degradation, soil carbon flux, and other biogeochemical drivers facilitating land cover change and earth system feedbacks. These multimodal approaches to knowledge discovery will not only improve sensitivity analyses and disentangle the spatial processes and causal links behind drivers of change, but also reconcile disparate estimations and below-ground uncertainty across the Arctic system.

During natural disasters, there is a noticeably increased use of social media sites such as Twitter. Substantial research on the use of social media data during disasters have been conducted in the past decade since various social media platforms have emerged and gained popularity. This research highlights a thorough examination of the textual content from millions of tweets shared on Twitter throughout the 48 contiguous U.S. states during hurricanes Harvey (2017) and Dorian (2019). Our analysis shows that there were sharp rises in twitter activities when the hurricanes made landfalls in the southeast US. We processed and analyzed 35 million tweets by categorizing them according to humanitarian topics over the Contiguous United States. Sentiment analysis, topic modeling, and topic classification are a few of the Artificial Intelligence techniques from Natural Language Processing (NLP) that we employed in this work to analyze the textual data produced by Twitter users during hurricanes Harvey and Dorian. Aided by these NLP techniques, this large volume of data made it possible to separate the tweet content into different categories in order to assist crisis management during and post disasters. Our study also offers a range of helpful insights gained at the state level before, during, and after the Hurricanes, which can aid disaster managers and responders in reducing the detrimental effects of the event and enhancing community readiness for it.

Geomagnetic storms are significantly influenced by the Earth's Ring Current. The incoming solar wind affects the structure and intensity of this dynamic ring-shaped environment on various time scales. Creating physics-based forecasting models for the ring current is particularly challenging since particle populations exhibit wildly different behavior. Satellite data provides electron point measurements that can be used to study the different physical processes occurring in the Earth's ring current. High temporal and spatial data resolutions are needed in order to properly comprehend the particle dynamics and injection processes. We attempt to tackle this issue by using a combination of electron-flux observations from different satellite missions and instruments in an effort to increase the global resolution of this dynamic environment. In this work we present a global reconstruction of the ring current population (energies between 1 to a few 100 keV) using data from RBSP, POES, GOES and THEMIS. We also show a comparison of the observed electron flux environment with a re-analysis of the ring current region obtained by using VERB-4D.

Understanding the role of firn microstructure on firn permeability is important for improving estimates of the gas-age ice-age difference for interpreting ice-core paleoclimate records, as well as the amount of open porosity available for meltwater storage on an ice sheet. Previous work has shown that current proxies to describe firn microstructure, such as open porosity and density, are insufficient metrics by which to capture gas diffusion through the firn column and that a better proxy could be intrinsic permeability (Adolph and Albert 2014). Intrinsic permeability is a quantitative property of a porous material that is constrained by the geometry and structure of the pores within the medium. It is, by definition, independent of the viscosity and density of a fluid moving through the open porosity. Obtaining intrinsic permeability data through the use of laboratory permeameters is time-consuming and can be prone to error if firn-core quality is poor. Rather, three-dimensional (3D) reconstructions of firn structure from Micro-CT imaging, allows us to use numerical simulations to calculate the firn�s intrinsic permeability rapidly. Here, we describe how to apply the Lattice Boltzmann Method to Micro-CT images of firn cores from NEEM, Greenland, to numerically estimate the intrinsic permeability of the firn. We highlight the influence of both image pre-preprocessing and numerical model selection on the quality of the calculated intrinsic permeability. Based on these observations, we highlight simulation choices that help to eliminate avoidable errors when numerically estimating intrinsic permeability of firn.

Micro-seismicity associated with hydrothermal systems (e.g., submarine volcanoes, mid-oceanic ridges, oceanic transform faults, etc.) share a complex relationship with the tidal forcing and is induced by fluid flow under different tectonic settings. The hydrothermal circulation drives the deformation at the brittle-ductile transition zone within a permeable brittle crust. Although the tidal loading magnitudes are too low to generate a brittle deformation, the incremental pressure exerted by the tidal loading can modulate the flow of hydrothermal fluid circulation and trigger the critically stressed faults or fracture zones. Here, we present a compelling case of tidal modulation in seismicity along the Blanco Ridge Transform Fault Zone (BRTFZ), a prominent linear segment in the northeast Pacific. The strong diurnal and weak semi-diurnal tidal periodicity is observed in the deeper seismic swarm (7-15 km). Whereas the shallow seismic swarm (0-7 km) exhibits a weak diurnal tidal periodicity. The dominance of diurnal periodicity in the deeper seismic swarm is explained by the high amplitude tidal cycles providing additional stress on the fluid circulation at the crust-mantle boundary. Moreover, our robust statistical correlation of micro-seismicity with tidal stress and resonance destabilization model under rate-and-state friction formalism suggests that the fault segments are conditionally unstable and more sensitive to periodic tidal stress perturbation due to the presence of hydrothermal fluid circulation in the serpentinized mantle.

Southern Indian Ocean Dipole (SIOD) is a dipole SST anomaly in the Southern Indian Ocean that plays an important role in tropical climate variability. However, previous studies focused on the interannual and interdecadal scales. In this paper, the characteristics of the sub-seasonal scale of SIOD are revealed based on the ERA-Interim and reanalysis data, and the excitation mechanisms of SIOD are also discussed. The results show that the two dominant modes of SST in the Southern Indian Ocean are the spatial distribution of Southwest-Northeast direction dipole (SIOD) and triple (SIOT) respectively, which has obvious periods of quasi 50-60 days. Moreover, the intensity and zonal oscillation of the Mascarene High are conducive to the formation of SIOD (SIOT). The air-sea interaction during the formation process is composed of three stages. In the first stage the atmospheric forces the ocean to result in the Mascarene High westward (eastward) and enhances the abnormal anticyclone in the Southern Indian Ocean, the surface latent heat flux release decreases (increases), and SST is warmed (cooled). During the following stage, the ocean feedback the atmosphere. The warm SST continues to increase, resulting in low-level convective enhancement, which weakens the abnormal anticyclone. The third stage is again the atmosphere forcing the ocean. The release of the latent heat flux increases (decreases) significantly which leads to the cooling (warming) of SST on the west (east) sides of an abnormal cyclone. In addition, the formation and extinction of SIOT are easier affected by the Southern Annular Mode(SAM) than SIOD. The abnormal zonal wave train with wavenumber 4 (3) propagates the Southern Indian Ocean by the westerly jet waveguide and results in an SST anomaly of SIOD (SIOT), accompanied by an obvious sub-seasonal meridional variation of the precipitation in Southern Africa.

River basin reorganization and changes in stream network characteristics alter the connections among aquatic species habitats. Cessation and resumption of streamflow, generation of physical flow barriers, and tectonic detachment of pre-established stream segments obstruct water-borne gene flow by limiting stream connectivity and therefore promote speciation. While little is known about the relationship between the rate and timing of speciation, and changes in ecohydrologic connectivity, we hypothesize that if there is a common pattern observed in river basin stream network changes and the evolutionary history of species over a long time, the cause and timing of speciation can be ascertained. In this study, we track the changes in the stream network in geologic timescale (i.e., 5 to 10 million years) to evaluate the ecohydrologic connectivity changes over time. We use a general landscape evolution program to simulate surface processes such as erosion and transport with time-varying tectonic motion and climate. A case study model is constructed to accommodate multiple drainage basins in a large model domain (up to 7.2 � 103 km2 in area) with a gentle slope forming a mountainous terrain. Beginning at an equilibrium state of the domain with tectonic uplift, erosion, and climate, we rigorously test various metrics that can represent flow (water) availability and stream disconnection throughout the model domain to suggest a representative metric for long-term ecohydrologic connection and disconnection. The observed patterns show that tectonics and climate, as two competing forcings, are the primary controls on the connectivity of streams. Sediments developed in topographic lows play a role in hampering streamflow and temporarily promoting disconnection. The reconstructed ecohydrologic connectivity metrics can place bounds on the timing of aquatic species evolution.

We present a statistical study characterizing the relationship between selected solar wind parameters and the properties of >50 keV electron isotropy boundaries observed in ELFIN data from Low-Earth Orbit. The isotropy boundary (IB) for an electron of a given energy is the magnetic latitude at which persistent poleward isotropized pitch-angle distributions (Jprec/Jperp~1) are first observed to occur, interpreted as resulting from non-adiabatic scattering due to field-line curvature at the magnetic equator (typically on the nightside extending to dawn/dusk). In this work we extend the recent study by Wilkins et al. (submission imminent) which characterized the statistical distribution of electron IB occurrence in L-shell, magnetic local time (MLT), and energy, as well as the amounts of precipitating energy flux from >50 keV energy electrons within and poleward of the IBs, which can be significant (at times exceeding all sources from latitudes below the IB). That work addressed the connection between IBs and geomagnetic activity indices (Dst, AE, and Kp), but did not investigate the direct connection to solar wind driving. Because the mechanism responsible for the generation of isotropy boundaries depends on the magnetic field conditions in the equatorial region, it is natural to expect solar wind driving of the magnetosphere to affect or control IB properties. We investigate this connection at both quiet and active times using the event database from the preceding Wilkins et al. study (over a thousand cases) to correlate with solar wind observables in OMNI data, focusing on the connection to solar wind velocity, dynamic pressure, interplanetary magnetic field, and electric field (among others). The spatial occurrence and precipitating energy flux distributions associated with IBs as a function of the solar wind observables are presented, alongside a discussion of proposed coupling mechanisms and timescales which relate the IB properties to solar wind driving. Results from this study can be used to inform a number of modeling efforts, including atmospheric energy input from energetic precipitating electrons under a range of solar wind conditions, as well as characterization of the instantaneous magnetic field configuration in the magnetotail and flanks.

A key uncertainty in the climate impacts of light absorbing aerosols relates to how they interact with water and, in particular, how particle-phase water alters their absorption. We will report on measurements and analysis of the hygroscopicity and optical properties of ambient aerosols observed during the ARM TRACER campaign in Houston, Texas in July 2022. Using a modified CAPS PMSSA, and a CRDPAS, we will quantify the increase in scattering, extinction and absorption, as well as impacts to single scattering albedo associated with increased humidity and connect the observed water uptake to the particle composition and mixing state.

Subsurface erosion, or soil piping, occurs as subsurface water flow erodes the soil, creating conduits or pipes. Piping is a major contributor to soil erosion in many parts of the world, and its collapse can lead to the formation of gullies or sinkholes. Subsurface erosion has been extensively studied for dams and levees; however, these studies often focus on saturated soils and water flow driven by reservoirs or rivers, whereas piping is commonly driven by rain or irrigation. The mechanics of piping in unsaturated soils remain poorly understood, which limits the ability of engineers and geoscientists to develop techniques to detect and monitor pipe formation and mitigate its effects. This study will present results from a series of experiments on model slopes with pre-existing pipes. These slopes are tested under precipitation loading using a rainfall simulator and subsurface flow through the pipe. The slope was instrumented with sensors to measure changes in suction, moisture content, and vibrations during the tests. The design and testing of the rainfall simulator will be discussed, along with data on erosion rates and pipe expansion in the slope during testing. This study is part of a larger project exploring the relationship between piping and landslide initiation by identifying the hydro-mechanical properties and interactions that control subsurface erosion. The data from this initial study will be used to design larger slope models that will test the relationship between piping and slope stability.

Sea ice area (SIA) is considered as an major indicator of climate change. Antarctic sea ice has not shown any significant trend until the last decade, but recent years have shown instances of anomalous sea ice retreat. We focus on the SIA changes in the marginal ice zone of East Antarctica to understand the drivers of these changes. Daily anomaly of SIA is calculated from satellite data(U.S. National Snow and Ice Data Center ) and compared against the 2-sigma levels of daily SIA time series to identify the anomalous seasons and years. Anomalous retreat occurred in Weddell Sea sector and Indian Ocean sector in the years 1983,1986,1990,2016 and 2017 during Austral Spring. To understand the synoptic conditions driving these changes, composite of geopotential height anomalies at 850 hPa and 10m wind anomalies were computed (using NCEP-DOE Reanalysis 2 data) over the East Antarctic sector during the anomalous seasons. In the Weddell Sea sector, which has the greatest amount of multiyear sea ice, the ice retreat was associated with a deep depression that gave strong northerly flow across the area. The atmospheric circulation at high southern latitudes was clearly anomalous in those years, and there is a strong correlation between the marked periods of northerly atmospheric flow and ice retreat.

The eruptions of Hunga Tonga-Hunga Ha�apai (HTHH) on 13-15 January 2022 generated waves in the atmosphere and ocean that were detectable as disturbances in Earth�s ionosphere. These Traveling Ionospheric Disturbances (TIDs) offer valuable information about acoustic and tsunami waves produced during the Tonga event. In the past decade, the use of Global Navigation Satellite Systems (GNSS) data has increasingly been utilized to explore ionospheric activity during natural hazard events. Many studies on this eruption sequence have focused on the climactic eruption and tsunami on 15 January or have highlighted the global reach of their TIDs. Given the complexities that arise due to wave-guiding, ocean bathymetry, and interference, we instead focus our analysis on TIDs over the southwestern Pacific and consider the full eruption sequence. Specifically, we use GNSS to explore the superpositioning and separation of acoustic- and tsunami-generated TIDs within ~3500 km of HTHH during 13-15 January. To do this, GNSS data were gathered from 818 stations managed by UNAVCO, IGS, Geoscience Australia, and GNS New Zealand. Raw data were processed using the SNIVEL_ION algorithm, while filtered time series were manually inspected to remove gross outliers. Lastly, tsunami arrivals were validated using DART (Deep-Ocean Assessment and Reporting of Tsunamis) buoy data where available. We find notably larger TIDs on 15 January, with perturbations up to ~7 TECu. Furthermore, we detect supersonic TIDs traveling at 833 m/s, with Lamb wave- and tsunami-generated TIDs following at 310 m/s. We also note a faster tsunami-generated TID that travels at 463 m/s, with a shift in the frequency domain, that crosses the slower TIDs ~3000 km from HTHH. Back-propagation shows that it took one hour for this faster TID to generate after the main eruption. We find that acoustic- and tsunami-generated TIDs begin to separate from each other at ~1000 km from HTHH and are distinct by ~2200 km from the source. Finally, we consider self-similarity between ionospheric signatures on the 13th and 15th to extract differences in the eruption history.

In recent decades, South Asian countries are witnessed unusual climate phenomena of increases in temperature and changing rainfall patterns. If adverse climate conditions prevail for a long period of time, an ecosystem becomes vulnerable in terms of primary productivity. The carbon and water use efficiency (CUE and WUE, respectively) are one of the key parameters to detect the impact of hydrological stress on ecosystem productivity. The current study is an attempt to investigate ecosystem resiliency at different scales in the SAARC region under hydrological stress conditions utilizing water and carbon use efficiency as indicating measures. The study included remote sensing datasets such as MODIS multispectral data products and gridded data of climatic parameters. The results of mean annual WUE and CUE for the research period (2001-2021) were found 0.66 � 0.34 g C kg-1 H2O and 0.45 � 0.21 (unitless), respectively. The spatio-temporal results depict that the water and carbon use efficiency is not affected in a very short period of time. Both have a strong but indirect relationship with climatic parameters like precipitation, land surface temperature, and actual and potential evapotranspiration. Potential evapotranspiration was found having the highest negative correlation with water and carbon use efficiency.

During the first science campaign of the Mars 2020 mission, Perseverance rover�s Scanning Habitiable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) deep UV Raman and fluorescence instrument collected microscale, two-dimensional Raman and fluorescence images on ten natural and abraded targets on two different Jezero crater floor units: S��tah and M�az. SHERLOC Raman images collected during the Crater Floor Campaign indicate that S��tah and M�az are mineralogically distinct igneous units with complex aqueous alteration histories. SHERLOC data show that S��tah is olivine-rich with evidence of olivine carbonation, while M�az features widespread silicates, but lacks olivine. Both units feature salt deposits. In S��tah, the salts are predominantly sulfate. Where salts are observed in M�az, they are a mixture of sodium perchlorate and sulfates. Taken together, SHERLOC data collected during the Crater Floor Campaign suggest that the Jezero crater floor once hosted an environment capable of supporting microbial life and preserving evidence of that life, if it existed.

In the era of citizen science, it is necessary that environmental protection data is made accessible to be analyzed and generate new discussions. The Mapbiomas is an efficient collaborative project that focuses on mapping deforestation of Brazilian biomes with techniques that deal with enormous amount of data, thus providing annual statistics of forest cover change for the whole country. The land uses that follow the forest cover removal are also mapped, basically agricultural and livestock. Aware of the potential use of those additional category�s statistics in land use planning and zoning, a research project was developed to evaluate the reliability of Mapbiomas� deforested land use classes and statistics. The selected area was the Buriticupu municipality, inserted in the Amazon Forest biome of Maranh�o State, Northern Brazil, with a territory of 254,500 hectares, which was object to an intense deforestation process and diverse land uses over the last forty years. The evaluation was performed by comparing Mapbiomas Collection 6 results for the years 1986, 2000, 2010 and 2020, with a detailed postprocessing class refining supervised classification of land use and forest vegetation of Buriticupu for the equivalent years, with single layer Landsat imagery. The comparison has showed a very good agreement of forest cover statistics, but a poor one for the different categories of small- and large-scale agriculture and livestock that usually follow the deforestation process. The main source of discrepancy derives from Mapbiomas� classification of small-scale agriculture, the so-called slash-and-burn agriculture, as either pasture or forest fragments. There is also an overestimation of pasture acreage, because of grouping small- and large-scale agriculture, and a difficulty to differentiate agribusiness land use from non-commodity medium size agriculture. The present work does not intend to undermine Mapbiomas� indispensable results, but to demonstrate that Mapbiomas� algorithms could be improved to extend an already useful large scale environmental management tool into an essential one for the regional and local scale monitoring and zoning planning.

The Mississippi River basin generates billions of dollars each year through manufacturing and agriculture and accommodates a large concentration of US energy infrastructure. This essential source of income faces an uncertain future as it grows more susceptible to flooding hazards via the combined effects of increasing sea level rise, storm surge, and intense precipitation events. A key challenge to predicting future hydroclimate conditions across the Lower Mississippi River basin is determining whether river discharge will increase or decrease during the 21st century; specifically, warming atmospheric temperatures will increase evaporation and reduce snowpack, but also increase extreme precipitation. Which effect will dominate? Because the 20th century record is limited in time, paleoclimate data and model simulations afford enhanced understanding of the basin�s hydroclimate response to external forcing. Here, we investigate how anthropogenic forcing in the 20th century shifts the statistics of river discharge compared to a last millennium (LM) baseline range for natural variability. Using simulations from the CESM - Last Millennium Ensemble, we evaluate changes in key hydroclimate variables including river discharge, snowmelt, precipitation, soil moisture, and runoff on monthly-to-centennial time scales. Anthropogenic (greenhouse gas, land use land cover, ozone) and natural (volcanic) single forcing simulations are used to identify the hydrological responses to each climate forcing individually. The full forcing simulations are also used to evaluate projected changes in the 21st Century. We find that greenhouse gas and land use changes generally led to wetter conditions in the 20th Century, compared to key LM periods (the Medieval Climate Anomaly and the Little Ice Age). While the 21st century is projected to have more extreme precipitation, reductions in snow melt, runoff and soil moisture result in drier conditions compared to the 20th Century. The additional climate information afforded by the Last Millennium simulations provides a larger pool of statistics, yielding improved predictive power. Understanding the external forcings that drove extreme flooding events in the past can help mitigate future risk and inform the development of regional flood mitigation strategies for the future.

Variations in the type and amount of carbon in coastal lake sediments represent changes in marine influence, human activities, or other environmental factors through time. The carbon stored in lake sediments is less well studied than that from other environments, such as peatlands and saltmarshes. However, existing studies suggest lakes store large amounts of carbon and are important carbon sinks. In this study we measured the organic and inorganic carbon content of sediments from sediment cores taken from Oliver Lake and West Beach Lake, which are located on Whidbey Island in Washington State. While these lakes are both coastal, West Beach is located at sea level and has more marine influence, and therefore higher inorganic carbon content throughout the core, than Oliver Lake which is located at the top of a cliff. In the cores studied, the carbon density of sediments ranged from 0.02 to 0.08 gC cm-3, with an average of ~0.05 gC cm-3. The cores indicate a general decline in organic carbon in the uppermost 10-20 cm, which we hypothesize to represent human activities that have introduced inorganic matter to the environment over the past ~20 years. We compare the distribution of carbon density values from our study to those from other published studies and show that the lake sediments measured have a higher average carbon density than those published for other coastal sediments in the area; however, additional data are needed to determine how representative these results are.

Large wildfires can have devastating impacts on infrastructure, air quality, and the environment. In the past few decades, the number of large fire years in Alaska, defined as a year with more than 1 million acres burned, has increased. Typical weather forecasts can tell fire managers what the fire conditions will be like for the next few days with good accuracy, but fire managers also need as much information as possible before the peak of fire season occurs in late June and early July to organize resources and personnel within the state. From our close work with the fire management community in Alaska, a user need for summer fire outlooks created as early as March was initially identified. As our work progressed, fire managers expressed a need for a supplemental outlook created in May. Here we compare the skill of the March-initialized seasonal forecasts to the May-initialized seasonal forecasts. For this study, we used seasonal forecasts at 3-month and 1-month leads to evaluate the skill of predicting fire activity for the years 1994-2019. Buildup Index (BUI), a fire weather index from the Canadian Forest Fire Danger Rating System, is used as a metric for fire danger. Three models were chosen based on the availability of data from their seasonal forecasts: the NOAA Climate Forecast System version 2, the European Centre for Medium-Range Weather Forecasts SEAS5, and the MeteoFrance System 8. Temperature and precipitation anomalies from the model ensembles were added to observed climatological values to provide more realistic values for Alaska. Daily BUI was then calculated from these temperature and precipitation values. Our preliminary results have shown skill in predicting BUI from the March-initialized seasonal forecasts, especially when combined into a multi-model ensemble, for various skill scores. We compare the skill scores of the two forecasts by region in Alaska and fire subseason for three BUI categories: above normal, normal, and below normal. We also examine how well each of the forecasts predict large fire years.

The Akobo gold deposit is situated in the NW-SE striking Surma shear zone of Western Ethiopian Precambrian terrain. Gold occurrence in this area is associated with structurally controlled quartz veins. The auriferous quartz veins are hosted in felsic metavolcanics and meta mafic-ultramafic rocks. Gold occurs mainly as discrete grains in the vein and within vein quartz fractures. This paper discusses the nature and evolution of CO2-bearing hydrothermal fluids responsible for gold deposition based on fluid inclusions study and Raman spectrometry. Fluid inclusion petrography and microthermometry of vein quartz samples were carried out at the Fluid Inclusion Laboratory, Department of Earth Sciences, IIT Roorkee, India. Raman spectroscopy for selected carbonic monophase and aqueous carbonic fluid inclusions was carried out at the Fluid Inclusion Laboratory of Wadia Institute of Himalayan Geology, Dehradun, India. Primary carbonic and aqueous carbonic fluid inclusions were further studied in the current study. Two types of primary inclusions were recognized based on the phase content, phase transitions during heating and freezing, and Raman spectra. These are, Type-I: carbonic monophase (CO2�CH4�N2) and Type-II: aqueous-carbonic (H2O-CO2�CH4�N2) inclusions. Type-I inclusions show TMCO2 varying from -62�C to -57�C and THCO2 in the range of -27.9�C to 22.8�C. The CO2 density ranges from 0.74 to 1.07g/cm3. Type-II inclusions have a melting temperature of CO2 (TM CO2) ranging from -61.2�C to -56.7�C and the temperature of homogenization of CO2 (TH CO2) ranges from -0.8�C to 22.3�C. Clathrate melting temperatures (TM clath) range from -9.2�C to 9.5�C, indicating salinities around 1.04 to 23.69 wt.% NaCl equivalent. Total homogenization (THtot) occurred between 185�C and 450�C. The presence of CH4 and N2 causes a decrease in TMCO2 in both types of inclusions. Medium to high temperature, low to medium salinity, and low-density CO2 fluids in the study area are similar to fluids involved in orogenic lode gold mineralization worldwide.

Curvature in tidal channels can create distinct flow processes including secondary circulation and flow separation. Both processes can have important influences on the along-channel momentum budget. Observations from the North River estuary, a sinuous tidal channel in Massachusetts, found much greater drag than is typical for similar estuaries with straight channels. We built a numerical model of the North River estuary to investigate how channel curvature increases momentum loss through complex three-dimensional flow processes. In channel bends, flow separation can generate a lee eddy that has lower pressure compared to the flow in the center of the channel, and this creates form drag for the along-channel flow. Form drag is the dominant source of drag increase during flood tides, but is less notable during ebb tides. Additionally, secondary circulation creates lateral and vertical velocities in the bend during both flood and ebb tides. The nonlinear advection by the secondary circulation transports high streamwise momentum from the surface to the lower water column. Consequently, the bottom boundary velocity structure deviates from a log-profile and the near-bed shear is enhanced. The increase in bottom stress due to redistribution of flow by the secondary circulation provides an additional sink for along-channel momentum. Both form drag due to flow separation and the enhanced bottom stress due to secondary circulation are important contributors to the increased drag. The increased momentum loss with channel curvature affects tidal propagation and the potential for flooding in tidal rivers and estuaries, along with the response of coastal systems to sea level rise and extreme events like storm surge. The altered boundary layer structure and enhanced bottom shear stress have important implications for sediment transport, channel morphodynamics, and material exchange across the coastal zone.

Waterfalls are striking features that can self-form in steep river channels. They are the dominant erosive force in many steep streams and transient knickzones. Waterfalls may alter river longitudinal profiles by eroding more or less quickly than a similar, non-waterfall reach, but it is not clear how factors such as lithology, sediment, waterfall geometry, and discharge create this difference. Here we measure waterfall erosion rates using concentrations of cosmogenic beryllium-10 (10Be) in bedrock from waterfalls in the Kings and Kaweah drainages in the Sierra Nevada, CA. We use detailed field surveys to identify dominant controls on erosion patterns and we compare local waterfall erosion to basin averaged rates derived from 10Be to determine whether the waterfalls are in steady state or disequilibrium with surrounding channels. This work will strengthen the interpretation of tectonic and climatic history from river profile morphology by helping to disentangle waterfall impacts on river longitudinal profiles.

Amazon forest accounts for around 40% of carbon stock in global tropical forests, playing a center role in uptaking anthropogenic carbon emission and mitigating global warming. Recent studies suggest that climate change has led Amazonia more susceptible to forest loss due to various drivers, and extreme storms is an important yet overlooked mode of tree mortality. Extreme storms produce downbursts and heavy rainfalls that may uproot or snap the trees. Field observations showed that extreme storms can explain ~50% of observed carbon residence time across the Amazon. In order to better understand how extreme storms influence forest dynamics, we used ED2 model to simulate forest growth in Amazonia with different settings of woody residence time (?w). We evaluated how spatial variability of ?w derived from the relationship with extreme storms shaped modeled patterns of aboveground biomass (AGB) and productivity. We found that with spatially varying ?w the model simulated lower AGB and stand-level wood density in western Amazon but higher AGB and stand-level wood density in eastern Amazon, which is closer to observed spatial gradients. Specifically, the model with spatially varying ?w simulated larger proportion of early successional trees but smaller proportion of late-successional trees in northwestern Amazon where more frequent extreme storms happened, confirming the effect of this mortality on ecosystem processes. In addition, spatially varying ?w enabled the model to simulate higher productivity and mortality in western but lower productivity and mortality in eastern Amazon, which are more comparable to observations, although current model parameterization tended to overestimate those rates. Our long-term simulations also predict larger carbon stock in Amazon forest at century-scale with spatially varying ?w, which suggested the necessity of addressing the changes of extreme storms and feedbacks on ecosystem processes when predicting future climate influence on global carbon budget.

Water resources are of great significance for hydrological planning and management, maintenance of ecosystem health, and sustainable social development. The increasing human water usage remarkably influences water resources under socioeconomic development. Water regulation policy is a common strategy to manage the increase in human water usage. In this study, the Community Water Model (CWatM), calibrated using historical monthly streamflow data, was used to explore changes in water resources in the Haihe River Basin. We investigated the change in hydrological processes driven by the daily weather data under various water usage and regulation scenarios. The results indicated that the spatiotemporal distribution and magnitude of hydrological processes were significantly influenced by water use. During the dry season, domestic and industrial usage reduced downstream streamflow by around 15 m3/s, and irrigation showed a more significant impact on soil water than other water usages. Water regulation had a more significant impact on surface water than groundwater. The streamflow could be reduced by 3-20% during the flood season and up to 50% during the dry season. Strict regulation might increase surface runoff by up to 150% in plain regions. Furthermore, different economic growth rates could result in a 20% variation in streamflow. The findings of this study highlight the need of practical water regulation policies to ensure sustainable water resources in the context of socioeconomic development and resulting water demand growth in the Haihe River Basin.

Microplastic are found from mountains to polar glaciers, with rivers being the main conveyors of plastic debris to the ocean. Plastics can go through settling, burial, and resuspension, defining the amount and type of plastics that get trapped within streams, and that will reach coastal areas. Negatively buoyant microplastic particles can deposit on the sediment bed, affecting sediment mobility and streambed morphodynamics. Flow-sediment-microplastic interactions were studied in two experimental facilities at the Ecohydraulics and Ecomorphodynamics Laboratory (EEL) at the University of Illinois at Urbana Champaign (UIUC): a turbulence tank for absent mean shear cases, and an Odell-Kovasznay recirculating flume for unidirectional flows. High-resolution velocity fields were measured with 2-dimensional Particle Image Velocimetry (PIV) to quantify turbulent features such as spatial velocity distribution, turbulent intensities, Reynolds stresses, turbulent kinetic energy, turbulent production, and shear velocities above the sediment bed. The sediment bed was composed of various mixtures of light-weight sediment (walnut shells) with different concentrations of embedded plastic particles, using two types of plastic with different densities. Time-lapse photographs were used to estimate the suspended sediment concentration (SSC), bed load, bed thickness variation, and percentage of exposed plastic for all mixtures. Plastic particles with lower density enhance the sediment mobility, until they get buried by bedforms, at which point the sediment mobility becomes independent of plastic concentration. In contrast, plastics of higher density provide armoring of the sediment bed once they are unburied. Our findings showcase the effect of different types of plastic particles on bed morphodynamics, providing the basis for a turbulence-based Shields-like parameterization to quantify sediment and plastic mobility. Keywords: microplastics, turbulence, unidirectional flows, coastal areas, suspension, morphodynamics.

Global climate models project temperature increases, but also increases in the frequency of extreme warm and cold weather events. Low temperature is one of the most influencing factors limiting plant distribution, and therefore understanding winter survival of plants under future climates is key. Low temperature adaptations involve both dormancy and cold hardiness. Dormancy is a mechanism to suppress growth during unfavorable conditions. Once dormancy is established, plants must also become cold hardy to survive low temperatures. While cold hardiness maximum appears to be a species characteristic, reaching it requires exposure to low, non-killing temperatures. The Spruce and Peatland Responses Under Changing Environments (SPRUCE) is a project comprised of large enclosures where the entire ecosystem is constantly warm to up to +9 �C above ambient temperature. Species from high latitudes are generally more sensitive to seasonal temperature patterns, and therefore SPRUCE presents an interesting environment to study how dormancy and cold hardiness will be affected in future warmer climates. Based on the chilling portions model, there were different patterns of chill accumulation across the warming treatments. Colder plots accumulated chill faster in the early season, but plateaued in mid-winter due to low temperatures, before resuming accumulation as temperatures started to rise in spring. Conversely, warmer plots constantly accumulated chill portions across the winter season once temperatures dropped enough in the fall. This suggests plants will be released from dormancy earlier in the season with warming. Cold hardiness was also affected by the warming of the treatments: species gained cold hardiness later and lost it earlier in warmer plots, although mid-winter hardiness was not affected by treatments. Earlier cold hardiness loss culminated in earlier budbreak of warmer plots. In species comparisons, Larix laricina loses cold hardiness faster than Picea mariana. Our data suggests that in future climate scenarios boreal forests will be more prone to damage during fall and spring, but not mid-winter, though at different magnitudes for different species.

Understanding how earthquakes initiate lies at the heart of earthquake physics, and analyzing foreshock sequences is one way to probe the initiation process of large earthquakes. A few oceanic transform fault (OTF) earthquakes have been observed to have more foreshocks compared to continental earthquakes. It has also been proposed that OTFs accommodate plate motion primarily by slow creep instead of rapid seismic slip, though with significant along-fault variability. These characteristics make OTFs unique laboratories for probing the physical processes underlying foreshocks and their relations with slow slip events. However, detailed studies at OTFs in the past have been limited due to their distance from land-based seismic stations. Since 2015, small arrays of ocean-bottom seismometers and hydrophones have been permanently deployed on cabled seafloor observatories in the northeast Pacific Ocean, allowing for monitoring of seismicity on the Blanco Transform Fault (BTF) using the earthquake�s radiated hydroacoustic energy (T-phase). T-phases also propagate through the SOFAR channel with little attenuation, allowing for detection of low magnitude earthquakes at large distances. In this study, we use a STA/LTA algorithm to first detect T-phase arrivals using their continuous waveforms recorded on both seismometers and hydrophones, before manually picking their arrival times. We then employ the GLOBAL mode Non-Linear Location (NonLinLoc) program for event localization using a 3D ocean sound velocity model. Next, using our catalog of hydroacoustic earthquake locations, we analyze the foreshock and aftershock sequences of 39 Mw>5 earthquakes that occurred along the BTF since 2015. Our goal is to quantify how the BTF sequences compare with earthquake sequences observed at continental transform faults. We also test the various models proposed to explain foreshock spatio-temporal behavior and the partitioning of seismic and slow slip along the BTF.