Joule heating deposits a significant amount of energy into the high-latitude ionosphere and is an important factor in many magnetosphere-ionosphere-thermosphere coupling processes. We consider the relationship between localized temperature enhancements in polar cap measured with the Resolute Bay Incoherent Scatter Radar-North (RISR-N) and the orientation of the IMF. Based on analysis of 10 years of data, RISR-N most commonly observes ion heating in the noon sector under northwards IMF Bz. We interpret heating events in that sector as being primarily driven by sunwards plasma convection associated with lobe reconnection. We attempt to model two of the observed temperature enhancements with a data-driven first principles model of ionospheric plasma transport and dynamics, but fail to fully reproduce the ion temperature enhancements. However, evaluating the ion energy equation using the locally measured ion velocities reproduces the observed ion temperature enhancements. This result indicates that current techniques for estimating global plasma convection pattern are not adequately capturing mesoscale flows in the polar cap, and this can result in underestimation of the energy deposition into the ionosphere and thermosphere.

We analyzed correlations between solar, interplanetary-medium parameters, and geomagnetic-activity proxies in 27-day averages (a Bartels rotation) for the 2009 – 2016 time interval. We considered two new proxies: I) Bzs GSM (Geocentric Solar Magnetic), calculated as the daily percentage of the IMF southward component along the GSM z-axis and then averaged every 27 days; ii) four magnetospheric indices (T-indices), calculated from the local north-south (X) contributions of the magnetosphere’s cross-tail (TAIL), the symmetric ring current (SRC), the partial ring current (PRC), and the Birkeland current (FAC), derived from the Tsyganenko and Sitnov (J. Geophys. Res. 110, A03208, 2005: TS05) semi-empirical magnetospheric model. Our results suggest, among the parameters tested here, solar facular areas, interplanetary-magnetic field intensity and new proxies derived here could be taken into account in an empirical model, with a 27-day resolution, to explain geomagnetic activity felt on the Earth’s surface in terms of solar surface features and the IMF condition. We further retrieve a clear annual oscillation in series of 27-day-mean values of toward/away asymmetries of geomagnetic-activity indices, which can be interpreted in the light of the Russell–McPherron hypothesis for the semiannual variation of geomagnetic activity (Russell, C.T., and R. L. McPherron (1973), J. Geophys. Res., 78, 92 – 108).

Soil biogeochemical models (SBMs) simulate element transfer processes between organic soil pools. These models can be used to specify falsifiable quantitative assertions about soil system dynamics and their responses to global surface temperature warming. To determine whether SBMs are useful for representing and forecasting data-generating processes in soils, it is important to conduct data assimilation and fitting of SBMs conditioned on soil pool and flux measurements to validate model predictive accuracy. SBM data assimilation has previously been carried out in approaches ranging from visual qualitative tuning of model output against data to more statistically rigorous Bayesian inferences that estimate posterior parameter distributions with Markov chain Monte Carlo (MCMC) methods. MCMC inference is better able to account for data and parameter uncertainty, but the computational inefficiency of MCMC methods limits their ability to scale assimilations to larger data sets. With formulation of efficient and statistically rigorous SBM inference frameworks remaining an open problem, we demonstrate the novel application of a variational inference framework that uses a method called normalizing flows to approximate SBMs that have been discretized into state space models. We fit the approximated SBMs to synthetic data sourced from known data-generating processes to identify discrepancies between the inference results and true parameter values and ensure functionality of our method. Our approach trades estimation accuracy for algorithmic efficiency gains that make SBM data assimilation more tractable and achievable under computational time and resource limitations.

Rapid northward drift of the Indian plate after 130 Ma has also recorded significant plate rotations due to the torques resulting from multiple vector force components. Seismic tomography of the Indian Ocean and palaeomagnetic database of the Deccan Traps are used here to constrain drift velocities at different temporal snapshots, resulting into estimates of 263.2 to 255.7 mmyr-1 latitudinal drift, 234 to 227.3 mmyr-1 longitudinal drift and 352.2 to 342.1 mmyr-1 diagonal drift, for the period from ~66 to 64 Ma during the Chrons C30n.y–C29n.y. Alternative displacement models suggest active driving forces arising from i) slab pull, ii) ridge push from eastern-, western and southern plate margins, and iii) Reunion plume-push force; in addition to delamination of the lithospheric root during approximately 65+2 Ma. Delamination of the root amplified the buoyancy of the Indian plate in contrast to sudden loading from Deccan basaltic pile that resulted into complex drift dynamics expressed by hyper plate velocities with an anomalous westward drift component of >342 mmy-1.

Agricultural water resources are threatened by climatic variability and increased competition for available freshwater resources. In order to mitigate the effect of climate change on cotton production, breeders are increasing their efforts on improving drought tolerance in this essential fiber crop. To achieve this, effective screening of diverse germplasm is needed to identify useful genetic variation that can be utilized for crop improvement. Within the last decade, unmanned aerial vehicles (UAVs) have led to the ability to quickly and reliably image large areas while simultaneously decreasing temporal effects associated with a large time window for data collection. This technology allows researchers to scale their phenotyping efforts, enabling studies that utilize mapping and monitoring efforts such as plant water stress detection. In this study, we used UAV-based thermal imagery to screen a diverse population of over 350 different genotypes of cotton in order to locate varieties that exhibit cooler canopies. This diversity panel was grown under two contrasting levels of irrigation, well-watered and water-limited, with data collection flights occurring weekly for three months during the season. The thermal images were clipped to plot boundaries, soil and plant pixels were segmented, and average temperatures were extracted to identify potential drought tolerant varieties. The objectives of this study were to (i) demonstrate that UAV-based thermal imagery, along with our calibration methods, can be used to render accurate plant canopy temperature values and (ii) identify cotton genotypes that outperform others in a drought-stressed environment.

Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.

Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.

Hyperspectral based prediction of nutrient content in maize leaves Hyperspectral imaging is a promising method to predict crop traits in a high-throughput manner and unlock quantitative genetic studies. A single hyperspectral image can be used to predict several unrelated traits at once using spectral data from 350nm - 2500nm. Researchers have successfully modeled different physiological traits in maize such as vegetative Nitrogen content but the effect of different development stages, genotypes, and treatments on modelling power remains unclear. Here, I explore the ability to model leaf macro- and micro- nutrient content and leaf water content from hyperspectral transmittance data collected with a LeafSpec imaging device. I will compare three different machine learning algorithms; Partial Least Squares Regression, Random Forest and a Convolutional Neural Net to model nutrient content collected from twenty hybrids throughout the 2020 field season in fertilized and not fertilized blocks. Genotypes and development stages excluded from model training are used to externally validate models. Sulfur, Nitrogen, Calcium, Copper, and Iron leaf concentrations were the most amenable nutrients to prediction with coefficient of determination scores from 0.78 - 0.73, respectively. Models trained on samples from a collection of time points were able to accurately predict new time points and genotypes. The findings demonstrate the ability to predict nutrient content in field grown maize over a variety of developmental stages, genotypes, and treatments from a handheld hyperspectral imaging device.

Firn densification profiles are an important parameter for ice-sheet mass balance and palaeoclimate studies. One conventional method of investigating firn profiles is using seismic refraction surveys, but these are limited to point measurements. Distributed acoustic sensing (DAS) presents an opportunity for large-scale seismic measurements of firn with dense spatial sampling and easy deployment, especially when seismic noise is used. We study the feasibility of seismic noise interferometry on DAS data for characterizing the firn layer at the Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to come from anthropogenic noise and shear-margin crevasses. The DAS cross-correlation interferometry yields a noisy Green’s function (Rayleigh waves). To overcome this, we present two strategies for cross-correlations: (1) hybrid instruments – correlating a geophone with DAS, and (2) selected stacking where the cross-correlation panels are picked in the tau-p domain. These approaches are validated with results derived from an active survey. Using the retrieved Rayleigh wave dispersion curve, we inverted for a high-resolution 1D S-wave velocity profile down to a depth of 100 m. The inversion spontaneously retrieves a “kink” (velocity gradient inflection) at ~12 m depth, resulting from a change of compaction mechanism. A triangular DAS array is used to investigate directional variation in velocity, which shows no evident variations thus suggesting a lack of deformation in the firn. Our results demonstrate the potential of using DAS and seismic noise interferometry to image the near-surface and present a new approach to derive S-velocity profiles from surface wave inversion in firn studies.

The presence of a pore fluid is recognized to significantly increase the mobility of saturated over dry granular flows. However, experimental studies in which both the bulk-scale (runout) and grain-scale behaviour of identical granular material in a dry and saturated initial state are directly compared are rare. Further, the mechanisms through which pore fluid increases mobility may not be captured in experimental flows of small volume typical of laboratory conditions. Here we present the results of dry and initially fluid saturated or ‘wet’ experimental flows in a large laboratory flume for five source volumes of 0.2 to cubic metre. Our results demonstrate that the striking differences in the nature of interactions at the particle scale between wet and dry flows can be directly linked to macro-scale behaviour: in particular, a greatly increased mobility for wet granular flows compared to dry, and a significant influence of scale as controlled by source volume. This dataset provides valuable test scenarios to explore the fundamental mechanisms through which the presence of a pore fluid increases flow mobility by first constraining the frictional properties of the material (dry experiments), permitting an independent evaluation of the implementation of interstitial fluid effects in numerical runout models (wet experiments).

Nitrous oxide (N20) is a greenhouse gas that is three hundred times more potent than carbon dioxide. The majority of N20 emissions worldwide are the result of excess soil nitrogen being metabolized by microbes. It has been hypothesized that crops with better nitrogen uptake efficiency and more roots will reduce excess soil nitrogen therefore reducing N20 emissions. To test this hypothesis, a pilot study was performed in 2021 in collaboration with Iowa State University in which root growth dynamics were captured using RootTracker ™ technology in four commercial maize hybrids. This preliminary study showed a correlation between increased root growth and reduced N20 emissions. Further, we find genetic differences in root growth that is consistent across reps, suggesting that i) cultivar choice impacts N2O emissions and ii) that it is possible to breed for root system architecture to limit N2O emissions. It was also observed that the hybrid with the fastest rate of root growth (lowest N20 emissions) did not reach the greatest soil depth, suggesting early root establishment could be pivotal to more efficient nitrogen uptake. These preliminary results suggest there are differences in root growth by variety that could be exploited to reduce agricultural N20 emissions at scale.

Despite numerous applications of Random Forest (RF) techniques in the water-quality field, its use to detect first-flush (FF) events is limited. In this study, we developed a stormwater management framework based on RF algorithms and two different FF definitions (30/80 and M(V) curve). This framework can predict the FF intensity of a single rainfall event for three of the most detected pollutants in urban areas (TSS, TN, and TP), yielding satisfactory results (30/80: accuracy average = 0.87; M(V) curve: accuracy average = 0.75). Furthermore, the framework can quantify and rank the most critical variables based on their level of importance in predicting FF, using a non-model-biased method based on game theory. Compared to the classical physically-based models that require catchment and drainage information apart from meteorological data, our framework inputs only include rainfall-runoff variables. Furthermore, it is generic and independent from the data adopted in this study, and it can be applied to any other geographical region with a complete rainfall-runoff dataset. Therefore, the framework developed in this study is expected to contribute to accurate FF prediction, which can be exploited for the design of treatment systems aimed to store and treat the FF-runoff volume.

Global ice volume (sea level) and deep-sea temperature are key measures of Earth’s climatic state. We synthesize evidence for multi-centennial to millennial ice-volume and deep-sea temperature variations over the past 40 million years, which encompass the early glaciation of Antarctica at ~34 million years ago (Ma), the end of the Middle Miocene Climate Optimum, and the descent into bipolar glaciation from ~3.4 Ma. We compare different sea-level and deep-water temperature reconstructions to build a resource for validating long-term numerical model-based approaches. We present: (a) a new template synthesis of ice-volume and deep-sea temperature variations for the past 5.3 million years; (b) an extended template for the interval between 5.3 and 40 Ma; and (c) a discussion of uncertainties and limitations. We highlight key issues associated with glacial state changes in the geological record from 40 Ma to present that require attention in further research. These include offsets between calibration-sensitive versus thermodynamically guided deep-sea paleothermometry proxy measurements; a conundrum related to the magnitudes of sea-level and deep-sea temperature change at the Eocene-Oligocene transition at 34 Ma; a discrepancy in deep-sea temperature levels during the Middle Miocene; and a hitherto unquantified non-linear reduction of glacial deep-sea temperatures through the past 3.4 million years toward a near-freezing deep-sea temperature asymptote, while sea level stepped down in a more uniform manner. Uncertainties in proxy-based reconstructions hinder further distinction of “reality” among reconstructions. It seems more promising to further narrow this using three-dimensional ice-sheet models with realistic ice-climate-ocean-topography-lithosphere coupling, as computational capacities improve.

Surface deformation accompanying dike intrusions is dominated by uplift and horizontal motion directly related to the intrusions. In some cases, it includes subsidence due to associated magma reservoir deflation. When reservoir deflation is large enough, it can form, or reactivate pre-existing, caldera ring-faults. Ring-fault reactivation, however, is rarely observed during moderate-sized eruptions. On February 21st, 2015 at Ambrym volcano in Vanuatu, a basaltic dike intrusion produced more than 1 meter of co-eruptive uplift, as measured by InSAR, SAR correlation, and Multiple Aperture Interferometry (MAI). Here we show that an average of ∼40 cm of slip occurred on a normal caldera ring-fault during this moderate-sized (VEI < 3) event, which intruded a volume of ∼24 million cubic meters and erupted ∼9.3 million cubic meters of lava (DRE). Using the 3D Mixed Boundary Element Method, we explore the stress change imposed by the opening dike and the depressurizing reservoir on a passive, frictionless fault. Normal fault slip is promoted when stress is transferred from a depressurizing reservoir beneath one of Ambrym’s main craters. After estimating magma compressibility, we provide an upper-bound on the critical fraction (f = 7%) of magma extracted from the reservoir to trigger fault slip. We infer that broad basaltic calderas may form in part by hundreds of subsidence episodes no greater than a few meters, as a result of magma extraction from the reservoir during moderate- sized dike intrusions.

This study proposed a quantitative model for analyzing the mass balance of large wood and its export on an annual scale at watershed scale. The study sites were five dam reservoir watersheds of the Kitakami River catchment located in the north-eastern part of Japan. From analyses on 20 years patterns of actual large wood export to target dam reservoirs, 1) the annual 24-hour maximum rainfall would be a key factor for the annual large wood recruitment, and 2) large wood export characteristics on an annual scale would be based on two relationships were found. Based on above findings, the model was consisted of two frameworks: the rainfall-induced analytical shallow landslide model for the large wood recruitment, and the double storage functions as with lumped hydrological method at a watershed scale for the large wood entrainment. The model was used to re-analyze 20 years patterns of large wood export observed at the dam reservoir watersheds. The Nash-Sutcliffe values were 0.7 or more, which is indicated high reproductivity of simulation, for large wood export estimated using this model for four among five dam reservoir watersheds. Namely, our results could demonstrate that the landslides are a significant procedure of large wood export, and the characteristics of large wood export can be defined by two relationships, which are the direct export of large wood caused by an increase in large wood recruitment with the extreme rainfall event, and the baseflow of large wood, which is mainly old large wood recruitment stored in the stream.

Distributed faulting typically tends to coalesce into one or a few faults with repeated deformation. The 2020 seismic sequence in southwestern Puerto Rico (SWPR) was characterized however by rupture of several short intersecting strike-slip and normal faults. The deformation does not appear to have coalesced despite several lines of geological and morphological evidence suggesting repeated deformation since post early Pliocene (~>3 Ma). The progression of clustered medium-sized (≥Mw4.5) earthquakes, modeling shoreline subsidence from InSAR, and sub-seafloor mapping by high-resolution seismic reflection profiles, suggest that the earthquake swarm was distributed across several fault planes beneath the insular shelf and upper slope in the vicinity of Guayanilla submarine canyon. The deformation may represent the southernmost part of a diffuse boundary, the Western Puerto Rico Deformation Boundary, which accommodates differential movement between the Puerto Rico and Hispaniola arc blocks. This differential movement is possibly driven by the differential seismic coupling along the Puerto Rico – Hispaniola subduction zone. We propose that the compositional heterogeneity across the island arc retards the process of focusing the deformation into a single fault. Given the evidence presented here, we should not expect a single large event in this area but similar diffuse sequences in the future.

Accreted ice retains and preserves traces of the ocean from which it formed. In this work we study two classes of accreted ice found on Earth—frazil ice, which forms through crystallization within a supercooled water column, and congelation ice, which forms through directional freezing at an existing interface—and discuss where each might be found in the ice shells of ocean worlds. We focus our study on terrestrial ice formed in low temperature gradient environments (e.g., beneath ice shelves), consistent with conditions expected at the ice-ocean interfaces of Europa and Enceladus, and highlight the juxtaposition of compositional trends in relation to ice formed in higher temperature gradient environments (e.g., at the ocean surface). Observations from Antarctic sub-ice-shelf congelation and marine ice show that the purity of frazil ice can be nearly two orders of magnitude higher than congelation ice formed in the same low temperature gradient environment (~0.1% vs. ~10% of the ocean salinity). In addition, where congelation ice can maintain a planar ice-water interface on a microstructural scale, the efficiency of salt rejection is enhanced (~1% of the ocean salinity) and lattice soluble impurities such as chloride are preferentially incorporated. We conclude that an ice shell which forms by gradual thickening as its interior cools would be composed of congelation ice, whereas frazil ice will accumulate where the ice shell thins on local (rifts and basal fractures) or regional (latitudinal gradients) scales through the operation of an “ice pump”.

State-of-the-art climate models simulate a large spread in the projected decline of Arctic sea-ice area (SIA) over the 21st century. Here we diagnose causes of this intermodel spread using a simple model that approximates future SIA based on present SIA and the sensitivity of SIA to Arctic temperatures. This model accounts for 70-95% of the intermodel variance, with the majority of the spread arising from present-day biases. The remaining spread arises from intermodel differences in Arctic warming, with some contribution from differences in the local sea-ice sensitivity. Using observations to constrain the projections moves the probability of an ice-free Arctic forward by 10-35 years when compared to unconstrained projections. Under a high-emissions scenario, an ice-free Arctic will likely (>66% probability) occur between 2036-2056 in September and 2050-2068 from July-October. Under a medium-emissions scenario, the ‘likely’ date occurs between 2040-2062 in September and much later in the 21st century from July-October.