We report a new series of experiments to explore the phenomenon of low-temperature annealing of fission tracks in apatite that feature a number of improvements over previous work. Grain mounts were pre-irradiated Cf to increase confined track detection and allow briefer thermal neutron irradiation. We co-irradiated and etched four apatite varieties (Durango, Fish Canyon, Renfrew, Tioga) over five time steps equally spaced from 3.66 to 15 ln(s). A length standard was co-etched with all experiments to ensure that subtle differences are within detection limits. Finally, we used a standard etching protocol, allowing the data to be co-modeled with extensive high-temperature data sets and recent analyses of induced tracks that underwent ambient-temperature annealing over year-to-decade time scales. Ambient-temperature annealing occurs at two different rates, with faster annealing at early stages that decreases to a slower rate that converges with empirical fanning linear or curvilinear models. The nature of this decrease varies among the apatite species examined, but no patterns could be determined. The fitted models make geological time-scale predictions consistent with those based on high-temperature data only, and also make predictions consistent with reasonable inferred low-temperature histories for all four apatite varieties. The empirical fanning curvilinear equation encompasses low-temperature annealing at month-to-decade time scales, but low-temperature annealing at shorter time scales may occur by a distinct mechanism. We consider but rule out annealing by radiation from short-lived activated isotopes. We also reconsider the notion of the initial track length, and the appropriate length for normalizing confined track length measurements.

Utilizing historical data from the U.S. nuclear test program and freely available barometric pressure data, we performed an analytical barometric-pumping efficiency analysis to determine factors resulting in late-time radionuclide gas seeps from underground nuclear explosions. We considered sixteen underground nuclear explosions with similar geology and test setup, of which five resulted in the measurement of late-time radionuclide gas concentrations at the ground surface. The factors we considered include barometric frequency and amplitude, depth of burial, air-filled porosity, intact-rock permeability, fracture aperture, and fracture spacing. The analysis indicates that the best discriminators of late-time radionuclide gas seeps for these explosions are barometric frequency and amplitude and air-filled porosity. While geologic information on fracture aperture and spacing is not available for these explosions, the sensitivity of barometric-pumping efficiency to fracture aperture indicates that fracture aperture would likely also be a good discriminator.

This study reports on the development and implementation of the HydroLearn online platform that supports active learning in the field of hydrology and water resources engineering. The platform is designed to serve the following two main purposes: to enable instructors to collaboratively develop and share active-learning resources, and to enhance student learning in fundamental and emerging topics in the field (e.g., rainfall-runoff processes, design of flood protection measures, flood forecasting, water-energy-food nexus). Using open-source technology, the HydroLearn platform supports customization of pre-developed learning modules and allows instructors to share components of their learning resources with other interested users. HydroLearn is inspired by the need to address challenges in adoption, scalability, and sustainability identified by research on educational innovations. HydroLearn utilizes research-based active learning methods (e.g., Problem-based Learning; Collaborative and Cooperative Learning) to create authentic online learning modules. The modules engage students in real-world hydrologic problems and provide unique opportunities to expose undergraduate students to modern hydrologic analysis tools that are at the forefront of hydrologic research and engineering practice. The platform includes tools that scaffold instructors’ implementation of sound pedagogical practices. The platform includes wizards and pre-populated templates on how to develop student-centered learning outcomes that ensure constructive alignment with the learning content. The platform also includes guidance for instructors on how to develop assessment rubrics to enhance student achievement through communicating the expected performance levels. The study will also share results on the implementation of a pilot learning module on flood protection. Thirty-six undergraduate students were surveyed before and after the implementation to determine their level of learning engagement. The survey measured their skills engagement, emotional engagement, participation, and performance engagement. The presentation will also report on efforts to engage the community through a fellowship program that aims to develop a network of educators who aspire to adopt active learning approaches and enhance hydrology education.

Solar Orbiter is an ESA/NASA mission that will provide an unprecedented opportunity to discover the fundamental connections between the rapidly varying solar atmosphere and the solar wind. The Solar Wind Analyzer (SWA) plasma package shall provide comprehensive in situ measurements of the solar wind. In particular, the Proton-Alpha Sensor (PAS) will determine the high time resolution properties solar wind ion populations through the measurement of the 3D distribution function, density, bulk velocity, temperature and heat Vux. The closest approach of Solar Orbiter to the Sun is 0.28 AU. At this distance the solar wind Vow, solar UV, and solar infrared Vuxes increase by a factor 13 compared to near-Earth space. The PAS instrument will provide high cadence 3D distribution function measurements (up to 13 per second) all the way from closest approach to 1 AU. The extreme UV and thermal conditions at closest approach precluded the use of standard ion optics schemes. We thus developed a new ion sensor optic design combined with conventional robust channeltron detectors. This paper describes the PAS measurement scheme adopted for varying solar wind conditions and our approach to the fast sampling of 3D distribution functions. We also highlight the expected scienti:c return of the instrument.

The north Ecuador subduction zone exhibits segmentation and clustering of seismicity through megathrust, interseismic, and aftershock seismicity. In 1906, a Mw 8.8 megathrust event ruptured a 500 km segment, portions of which were re-ruptured in ’42 (Mw 7.8), ’58 (Mw 7.7), ’79 (Mw 8.2) and 2016 (Mw 7.8 Pedernales event). Segmentation between the ruptures is caused in part by subducting topography and upper plate structure. Upper plate structure in north Ecuador includes major faults, sedimentary basins and accreted terranes. An international aftershock deployment and the Ecuador permanent network (RENSIG) recorded aftershocks of the 2016 Pedernales event. We performed finite difference tomography in a joint inversion for 3D velocity and earthquake location, using body wave arrivals of aftershocks. The Manabi, Manta-Jama and Borbon sedimentary basins are observed as high Vp/Vs features with the Manabi basin seen as a low Vp and Vs feature. High Vp and Vs are associated with accreted forearc terranes. Relocation of aftershocks in the 3D velocity results in previously described “bands” of seismicity collapsing to smaller clusters ranging from ~8-40 km across. South of the rupture area, a cluster near Manabi collapsed landward, and a cluster appeared west of the trench. Three clusters between the trench and directly south of the rupture contain lower plate and plate interface events. The cluster within the rupture area between the patches of greater slip became more focused, and a cluster became defined on the north side of the northern patch of slip. Two clusters outline subducting Atacames seamounts, with events in the lower plate and interface beneath and in front of the seamounts. North of the rupture, the clusters offshore and onshore near Galera contain mostly interface with some upper plate events. The onshore cluster focused around major faults in a transition from north/south to northeast/southwest structures along the coastal range. Events in the cluster near Atacames relocated mainly in the upper plate, and events in the cluster near Esmeraldas remained in the upper plate. Interseismic events cluster in the same locations as aftershock events. Existing features including upper plate structure and subducting features control and focus both postseismic and interseismic deformation across megathrust cycles.

China experienced heavy aerosol pollution in recent years. The aerosol pollution might change surface solar radiation and impact the land carbon cycle in China. Therefore, we combined the MODIS Atmosphere and Land products with process-based model to evaluate the sensitivity of GPP to AOD in different spectral bands under all-sky and cloudless conditions and the effects of current aerosol loadings on GPP at site level. Our results indicated that the radiative effects of aerosols on GPP varied with the vegetation type, which is consistent with other studies. We also found that the radiative effects of aerosols on GPP varied with the spectral bands. Cloud restrains the effects of aerosols on growth of plants. Current aerosol loadings fertilized the growth in two forest growth and slightly impede the growth of grass. The results in this paper could be help to fully understand the influences of aerosol on land carbon cycle.

During each International Ocean Discovery Program (IODP) expedition a vast array of data, typically amounting to hundreds of gigabytes to several terabytes of information, are collected from drill cores. These data include physical, chemical, and magnetic properties and digital images collected continuously or every few centimeters along the cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. Coring just since the start of Expedition 349 in January 2014 has recovered over 50 km of core, resulting in a very large amount of data, most of which are accessible from the IODP LIMS database. Some of the properties typically measured include P-wave velocity, density, magnetic susceptibility, natural remanent magnetization, natural gamma radiation, and visible spectral reflectance. In addition, the lithology of all cores is described based mainly on visual characteristics of the surface of the split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. Our goal in this study is to mine these data for interrelationships that would otherwise be difficult to assess given the way the data are partitioned by specific property within the database. In particular, we extract basic lithologic information from the complex array of descriptive information and then tie that information to all other observations in order to characterize the physical, chemical, and magnetic properties of a myriad of lithologies.

The variety of missions and observational campaigns in Earth and Space Science has led to a vast number of files containing low-level datasets with native, incompatible arrays. While higher-level datasets re-interpret this low-level data onto common, comparable arrays, this standardization moves scientists farther away from the observations, constraining the analysis and science that can be performed. SpatioTemporal Adaptive Resolution Encoding (STARE) provides a parallelizable, scalable index for co-aligning data with different native spatiotemporal formats efficiently across distributed computing resources. STARE is particularly useful for opening low-level datasets to intercomparison and integrative analysis by providing “array” indexes that carry spatiotemporal semantics, unifying datasets with previously incomparable native array indexing. We are developing STARE as a software library with both C++ and Python APIs and are integrating STARE indexing with existing data transfer tools (OPeNDAP). By organizing data in a hierarchical format and taking advantage of the Hierarchical Data Format’s (HDF) virtualization features, STARE may provide an end user with familiar HDF usability with STARE-enhanced performance and data unification on the back end. Furthermore, STARE’s spatial encoding can be used to index and integrate datasets associated with other planetary bodies, bringing scalability and unification of diverse data for planetary and space science as well.

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing-sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P) increases, and increased E reduces soil moisture. E and D are generally more elastic to changes in P than E. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices.

The canonical picture of the magnetotail of unmagnetized planets consists of draped interplanetary magnetic fields (IMF) forming opposite-directed lobes, separated by the current sheet. \citet{DiBraccio2018twisted} showed that Mars’ magnetotail has a twist departing from this picture. Magnetohydrodynamic (MHD) results suggest that open field lines connected to the planet that populate portions of the tail cause the apparent twist. To validate this interpretation, we compare the tail topology determined from MHD simulations to that inferred from data collected by the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft, in particular how each topology responds to the upstream IMF orientation. The occurrence rates for open topology from both data and MHD varies with IMF polarities in a similar fashion as the tail twisting. This suggests that Mars’ crustal fields have a global effect on the magnetosphere configuration, supporting the picture of a “hybrid” magnetotail that is partly induced/draped and partly intrinsic/planetary in origin.

Substantial changes have occurred in the Arctic Ocean in the last decades. Not only sea ice has retreated significantly, but also the ocean at mid-depth showed a warming tendency. By using simulations we identified a mechanism that intensifies the upward trend in ocean heat supply to the Arctic Ocean through Fram Strait. The reduction in sea ice export through Fram Strait induced by Arctic sea ice decline increases the salinity in the Greenland Sea, which lowers the sea surface height and strengthens the cyclonic gyre circulation in the Nordic Seas. The Atlantic Water (AW) volume transport to the Nordics Seas and Arctic Ocean is consequently strengthened. This enhances the warming trend of the Arctic AW layer, potentially contributing to the “Atlantification” of the Eurasian Basin. Therefore, the Nordic Seas can play the role of a switchyard for the Arctic sea ice decline to influence the Arctic heat budget at mid-depth.

Landslides exhibit complex geomorphological features, which are difficult to monitor and map the changes. Photogrammetric methods have emerged as promising tools to overcome such problems due to 3D reconstruction from overlapping images without disturbing the surface. This study presents the structure from motion (SfM) technique for three-dimensional reconstruction using video and images of a large landslide which occurred on 13 August 2017 at Kotropi, Himachal Pradesh, India. In this study, we have used an Unmanned Aerial Vehicle (UAV) – DJI Phantom 3 Advanced to collect high-resolution images and video of landslide. A total of 98 images and 9 videos of 12:18 minutes duration were captured during the drone flight on 23 March 2019 covering the whole landslide and surrounding areas. The images were collected in unorganized manner for covering the whole landslide in the limited amount of time. We have used videos for coarse reconstruction and high-resolution images for fine reconstruction of landslide. The whole model was developed in two parts using MeshRoom and later merged using co-registration in CloudCompare. Based on feature detection technique, scale invariant feature transform (SIFT), image features were automatically detected, described, and matched between photos. A bundle block adjustment is performed on the matched features to identify the 3D position and orientation of the cameras, and the XYZ location of each feature in the photographs resulting in a sparse 3D point cloud. Finally, meshing is carried out using 3D Delaunay tetrahedralization. For visualization and analysis of final 3D model, open source software CloudCompare/MeshLab was used. The morphological parameters such as length, width, height, perimeter, area and volume were computed from the 3D model. From field observations and image analysis we will show that UAV-based image in combination with 3D scene reconstruction algorithms provide a flexible and effective tool to map and monitor large landslides such as Kotropi landslide of Himachal Pradesh.

Landslides cause billions of dollars in property damage and thousands of deaths every year worldwide. India has more than 15% of its land area prone to landslides, hence mapping of these areas for the presence of landslides is of utmost importance. Landslide susceptibility zonation maps give approximate information about the occurrence of landslides. There are various factors responsible for slope instability. In this work, 11 causative factors have been considered such as Aspect, Elevation, Geology, Distance from thrusts, Distance from streams, Plan curvature, Profile curvature, Slope, Stream power index, Tangential curvature, Topographic wetness index. Machine learning methods such as artificial neural network, support vector machine require a large amount of training data; however, the number of landslide occurrences are limited in a study area. The limited number of landslides leads to a small number of positive class pixels in the training data. On contrary, the number of non-landslide pixels (negative class pixels) are huge in numbers. This under-represented data and severe class distribution skew create a data imbalance for learning algorithms and sub-optimal models, which are biased towards the majority class (non-landslide pixels) and have low performance on the minority class (landslide pixels). Generally, the data is imbalanced when the class ratio is of the order of 100:1, 1000:1 and 10000:1 (i.e., one-class points are 100, 1000 or 10000 times more than that of another class points). In our work, class ratio is more than 300:1 (i.e. for each one landslide pixel, we have more than 300 non-landslide pixels). Thus, we can clearly say that our data is imbalanced. There are two major data balancing techniques, which are oversampling of a minority class and under-sampling of majority class. The minority oversampling cannot be applied, as it will create false landslide pixels. We have performed under-sampling of non-landslide pixels using various techniques. We will discuss landslide susceptibility zonation with and without using data imbalance technique and show major improvements in accuracy over imbalanced learning.

The January 15, 2022 phreatomagmatic eruption of the submarine Hunga Tonga-Hunga Ha’apai (HTHH) volcano (Tonga) generated an explosion of historic magnitude, and was preceded by ~1 month of Surtseyan eruptive activity and two precursory explosive eruptions. We present an analysis of ultraviolet (UV) satellite measurements of volcanic sulfur dioxide (SO2) between December 2021 and the climactic January 15, 2022 eruption, comprising an unprecedented record of Surtseyan eruptive emissions. UV measurements from the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite, the Ozone Mapping and Profiler Suite (OMPS) on Suomi-NPP, the Tropospheric Monitoring Instrument (TROPOMI) on ESA’s Sentinel-5P, and the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) are combined to yield a consistent multi-sensor record of SO2 emissions during the eruptive sequence. We estimate SO2 emissions during the key phases of the eruption: the initial December 19, 2021 eruption (~0.01 Tg SO2); continuous SO2 emissions from December 20, 2021-early January 2022 (~0.12 Tg SO2); the January 13, 2022 stratospheric eruption (0.06 Tg SO2); and the paroxysmal January 15, 2022 eruption (~0.4-0.5 Tg SO2); yielding a total SO2 emission of ~0.6-0.7 Tg SO2 for the entire eruptive episode. We interpret the vigorous SO2 emissions observed prior to the January 2022 eruptions, which were significantly higher than measured in the 2009 and 2014 HTHH eruptions, as strong evidence for a rejuvenated magmatic system. High cadence DSCOVR/EPIC SO2 imagery permits the first UV-based analysis of umbrella cloud spreading and volume flux in the January 13, 2022 eruption, and also tracks early dispersion of the stratospheric SO2 cloud injected by the January 15 eruption. The ~0.4-0.5 Tg SO2 discharged by the paroxysmal January 15, 2022 HTHH eruption is low relative to other eruptions of similar magnitude, and a review of previous submarine eruptions of the satellite era indicates that such modest SO2 yield may be characteristic of these events, with the emissions and atmospheric impacts likely dominated by water vapor (WV). The origin of the low SO2 loading awaits further investigation but scrubbing of SO2 in the water-rich eruption plumes and rapid conversion to sulfate aerosol are highly plausible, given the exceptional WV emission measured in the January 15, 2022 HTHH eruption.

Tropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme winds, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics-based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics-based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high-resolution wind datasets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950-2020 using a basin-scale hydrodynamic model and show that our reconstructions agree well with observed peak water levels. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.

Reconstructions of river discharge and other hydrologic variables often exploit large available networks of tree-ring chronologies from multiple species and hydrologic settings. A common early step in such studies is screening to reduce the predictor data set and focus on chronologies with a strong hydrologic signal. A stepwise regression approach to screening is proposed and illustrated for reconstruction of April 1 snow-water equivalent (SWE) at three snow courses in the northern Sierra Nevada and Lake Tahoe region from a multi-species tree-ring network. SWE is regressed separately on each chronology lagged t-2 to t+2 years from the year of SWE. A chronology is accepted based on specified criteria for temporal stability of signal and skill of the lagged model in predicting SWE outside the calibration space. A cross-validation stepwise cutoff rule is applied to guard against over-fitting the lagged model. Illustration for a network of 23 chronologies of five snow-adapted species (Juniperus occidentalis, Pinus jeffreyi, Pinus ponderosa, Abies magnifica, and Tsuga mertensiana) underscores the critical importance of lags in the tree-ring response to SWE. For Abies and Tsuga, in particular, chronologies passing screening are characterized by lagged models with a positive coefficient on the year following the hydrologic anomaly (deep snowpack this year, wide ring the following year) and no dependence or a negative coefficient on the current year (deep snowpack, narrow current ring). The SWE signal is strongest for one particular Juniperus chronology whose regression explains 39% of the SWE variance at two snow courses. The strength of SWE signal varies greatly over sites within species. More than half of the Juniperus and Pinus chronologies were rejected by screening because of either weak or temporally unstable signal. A repeat of the regression screening using water-year precipitation (Global Historical Climate Network) instead of SWE suggests that different subsets of chronologies are optimal depending on the target hydologic variable. Few chronologies have a significant signal for the residual of SWE regressed on water-year precipitation. This suggests little snow-specific information on the moisture signal in annual ring width. Sub-annual ring measurements and quantitative wood anatomy are suggested as possible ways to help discriminate the rain and snow signals in tree rings from the region.

Projections of future sea-level change are characterized by both quantifiable uncertainty and by ambiguity. Both types of uncertainty are relevant to users of sea-level projections, particularly those making long-term investment and planning decisions with multigenerational consequences. Communicating information about both types is thus a central challenge faced by scientists who generate sea-level projections to support decision-making. Diverse approaches to communicating uncertainty in future sea-level projections have been taken over the last several decades, but the literature evaluating these approaches is limited and not systematic. Here, we review how the Intergovernmental Panel on Climate Change (IPCC) has approached uncertainty in sealevel projections in past assessment cycles and how this information has been interpreted by national and subnational assessments, as well as alternative approaches used by recent US subnational assessments. The evidence reviewed here generally supports the explicit approach to communicating both types of uncertainty adopted by the IPCC Sixth Assessment Report (AR6).

The COVID-19 pandemic has caused concerns globally. However, there are some positive effects because of the outbreak such as cleaner air caused due to the lock-down. The pandemic and consequent lock-down (LD) has caused significant reduction in anthropogenic activities, which provided an unprecedented opportunity to study the contribution of manmade short-lived climate forcing agents from two distinct environmental locations; Bengaluru, a mega city and rural continental region, Challakere, in southern part of peninsular India. We have used continuous and simultaneous analytical measurements of the aerosol loading and optical properties of near-surface aerosols for this purpose. The near total shutdown of rail, road, and air traffic as well as total closure of most of the business establishments and IT industry, is found to dramatically reduce black carbon (BC) aerosol abundance. Within one week of the LD, urban Bengaluru has witnessed nearly 60% reduction in BC from fossil fuel (BCff) emissions, which is almost comparable to that at a rural region like Challakere, located about 230 km northwest to Bengaluru. BC concentration at Challakere did not show any major impact because of the lock-down. On other hand, BC from biomass burning (BCwb) from both the locations didn’t show any conspicuous impact. Consequently, the fraction of BCwb to total BC became more than double and spectral absorption coefficient increased from ~1.15 to ~1.4. The single scattering albedo increased from its prevailing mean value of ~0.66 before LD to 0.74 during LD1 and then gradually decreased with more relaxations. The measurements also have shown the manner in which the environment responded to the gradual relaxation in the subsequent phases of LD leading to gradual increase in emissions. The findings re-emphasize that the emissions from fossil fuel combustion in industrial and automobile sector are the major source of absorbing aerosols over urban and semi-urban environments.