Soil water flow and physical quality as influenced by soil water retention curve fitting methods

The triggering of large earthquakes on a fault hosting aseismic slip or, conversely, the triggering of slow slip events (SSE) by passing seismic waves involves seismological questions with important hazard implications. Just a few observations plausibly suggest that such interactions actually happen in nature. In this study we show that three recent devastating earthquakes in Mexico are likely related to SSEs, describing a cascade of events interacting with each other on a regional scale via quasi-static and/or dynamic perturbations. Such interaction seems to be conditioned by the transient memory of Earth materials subject to the “traumatic” stressing produced by the seismic waves of the great 2017 (Mw8.2) Tehuantepec earthquake, which strongly disturbed the aseismic slip beating over a 650 km long segment of the subduction plate interface. Our results imply that seismic hazard in large populated areas is a short-term evolving function of seismotectonic processes that are often observable.

Meltwater retention in the firn layer of the Greenland Ice Sheet is has the potential to buffer sea level rise due to ice sheet melt. The capacity of the firn layer to store meltwater is unclear, however, because refrozen ice layers can act as impermeable barriers to meltwater percolation, promoting runoff rather than retention. We present time-domain reflectometry and thermistor data which demonstrates that meltwater successfully penetrates ice layers up to 12 cm thick in the near-surface firn at Dye2, Greenland. Our observations indicate that ice layers within polar firn can become permeable when summer warming and latent heat release from refreezing meltwater raise temperatures to the melting point. This facilitates meltwater retention, and indicates that the depth of penetration of the summer melting front (the 0°C isotherm) represents the primary control on meltwater infiltration in the percolation zone of the Greenland Ice Sheet.

Turbulent mixing at the sub-meter scale is an essential component of the ocean’s meridional overturning circulation and its associated global redistribution of heat, carbon, nutrients, pollutants and other tracers. Whereas direct turbulence observations in the ocean interior are limited to a modest collection of field programs, basic information such as temperature, salinity and depth is available globally. Here, we show that supervised machine learning algorithms can be trained on the existing turbulence data to develop skillful predictions of the key properties of turbulence from $T,S,Z$ and topographic data. This constitutes a promising first step toward a hybrid physics-artificial intelligence approach to parameterization of turbulent mixing in climate models.

In this study, flume experiments were conducted under conditions where alternate bars occur, develop, and migrate, to understand the existence and scale of the spatial distribution of the migrating speed of alternate bars and their dominant physical quantities. In the flume experiment, the bed level and water level during the development of alternate bars were measured with high frequency and high spatial resolution. By comparing the geometric variation of the bed shape, the results showed that the migrating speed of the alternate bars is spatially distributed and changes with time. Next, to quantify the spatial distribution of the migrating speed of the alternate bars, a hyperbolic partial differential equation for the bed level and an calculating equation the migrating speed based on the advection term of the same equation were derived. Subsequently, the derived equation was shown to be applicable by comparing it with the measurements obtained in the flume experiments described above. The migrating speed of the alternate bars was calculated using above formulas, and it was found to have a spatial distribution that changed with the development of the alternate bars over time. The mathematical structure of the equation showed that the three dominant physical quantities of the migrating speed are the particle size, Shields number, and energy slope. In addition, our method is generally applicable to actual rivers, where the scale and hydraulic conditions are different from those in the flume experiments.

Mars’ controversial hypothesized ocean shorelines have been found to deviate significantly from an expected equipotential surface. While multiple deformation models have been proposed to explain the wide range of elevations, here we show that the historical locations used in the literature and in these models vary widely. We find that the most commonly used version of the Arabia Level does not follow the originally described contact and can deviate laterally by ~500 km in Deuteronilus Mensae. A meta-analysis of different published maps shows that, globally, the minimum lateral offsets between the locations of the putative Arabia and Deuteronilus shorelines vary by an average of 141±142 km and 180±177 km, respectively. This leads to mean elevations of the Arabia Level that vary by up to 2.2 km between different mappings, and topographic ranges within each global mapping ranging from 2.7 to 7.7 km. The younger Deuteronilus Level has less topographic variation as it largely follows a formal contact (the Vastitas Borealis Formation) within the relatively flat northern plains. Given the high variance in position (spatial and topographic) of the maps, the use of such data and conclusions based on them are potentially problematic.

The Gandalf Staff is a mobile tool designed to be a flexible device supporting crewed and uncrewed operations on the lunar surface. The core of the device is a 24v battery with communications and data storage systems. Initial optional components supporting crewed Extra-Vehicular Activity (EVA) include a LiDAR and 360˚ camera. These provide 3D mapping of the traverse for documentation, and to aid future planning. The mapping also creates outreach opportunities for the public to “stand beside” the astronaut in Virtual Reality (VR). The staff provides external lighting for field site illumination in the south polar region low sun angle environment. Navigation instruments for crew position determination with Lunar Search and Rescue (LunaSAR) are also included. The staff itself can be used as a walking aid or as a splint for Incapacitated Crew Rescue (ICR). As a stand-alone device, the staff operates as a long duration untended science platform collecting environmental data and sending it to a lunar base station. The stand-alone mode requires connection to an auxiliary power source (e.g. solar array) and energy storage system (e.g. battery), so it could become an electrical recharging station. To make rapid progress in the 1st year, and also to demonstrate innovative project management techniques, NASA guided a private industry partner, T STAR, in leading Capstone Engineering student teams at Texas A&M University (TAMU) for proof-of-concept development and testing. These teams developed the power system and demonstrated successful integration of LiDAR, WiFi communications, and external lighting subsystems. Another industry team at Jacobs Technology prototyped a tripod to hold the staff upright. For the 2nd year (FY’22), NASA will again collaborate with partners to prototype enhanced power and lighting concepts. Year 2 will also add new capability for LunaSAR and geophysical science instrumentation using a heat probe. The heat probe is based upon Apollo heritage but modified to measure subsurface volatile ice regimes at the Artemis landing site. Components of the Gandalf Staff can be developed, tested, and deployed independently, or on the integrated staff, rovers, or utility trailers. The project supports crew safety, lunar sample curation, mission science, and public outreach goals of NASA.

The hydrological quantities governing the generation of riverbed waves (formed spontaneously on the bottoms of rivers) have been elucidated through geomorphological methods, laboratory experiments, stability analyses, numerical analyses, and other research methods.Recently, numerical analysis was performed with a fine spatial resolution.However, numerical analysis cannot always describe the real phenomena because it is based on assumptions.Therefore, understanding the physical phenomena by measurements with the same resolution as the numerical analysis is necessary.Measurement data with high resolution enable the construction of a duplicate of the measurement target on a computer, called a “digital twin”.To construct a digital twin of the process of riverbed wave generation and development, the geometries of the water surface and the river bottom must be measured simultaneously.We developed and verified a measurement method for the construction of a digital twin during the generation and development of riverbed waves.The measurement system uses two cameras and a line laser to simultaneously measure the water surface and river bottom.Accurate refraction correction at the water surface is possible by acquiring the shape of the water surface, allowing the bottom shape to be determined by geometric processing.The method provides submillimeter-accurate measurements of the water surface and bottom with a spatial resolution of 0.95 cm longitudinally and 0.038 cm transversely in a 12 m $\times$ 0.45 m channel and takes only one minute per measurement.This method can provide measurement results that contribute to the understanding of the formation and development of riverbed waves.

The originality of this study is to propose a new calibration method based on two calibration phases between Jason-3 and Sentinel-6A (S6A) to better estimate the relative global and regional mean sea level drifts between the two missions. To date, a first calibration phase of approximately 12 months is planned from January 15, 2021, to December 31, 2021, when both satellites will be on the same orbit spaced out by approximately 30 seconds. This calibration will allow for a very accurate assessment of the GMSL bias between Jason-3 and S6A (less than 0.5 mm, see ​ Zawadzki and Ablain, 2016​). A second calibration phase after a few years would reduce the uncertainty levels of the GMSL (global mean seal level) drift estimate. The uncertainty would be low enough to detect any drift detrimental to the stability of the current GMSL record. It would indeed be possible to evaluate the stability between the two satellites with an accuracy at least 3 times better at the global scale than with the most accurate method to date. At regional scales, the second calibration phases would provide regional MSL drift estimates with very good precision. This study also shows that the time spent between the two calibration phases is significantly more sensitive than the length of the second calibration phase for the reduction in uncertainties. Finally, a possible scenario proposed by this study would consist of carrying out the beginning of the second calibration phase approximately 1.5-2 years after the first and for a duration of 3-4 months. This calibration would allow the detection of a relative GMSL drift of approximately 0.15 mm/yr and 0.4-0.5 mm/yr at oceanic basin scales (2000-4000 km).

Coarse-grained quartz veins from the Prijakt Nappe (Austroalpine Unit, Schober Mountains, Eastern Alps), that formed under amphibolite facies conditions, were overprinted by lower greenschist facies deformation. During overprinting, subgrain rotation (SGR) recrystallization was the dominant mechanism assisting the evolution from protomylonite to (ultra)mylonite. The initial Ti-concentration [Ti] (3.0-4.7 ppm) and corresponding cathodoluminescence (CL) signature of the quartz vein crystals were reset to different degrees mainly depending on the availability of fluids and their partitioning across the microstructure. The amount of strain played a subordinate role in resetting. In recrystallized aggregates the most complete re-equilibration ([Ti] of 0.2-0.6 pm) occurred in strain shadows surrounding quartz porphyroclasts, acting as fluid sinks, and in localized shear bands that channelized fluid percolation. We applied a correlative multi-analytical workflow using optical and electron microscopy methods (e.g. electron backscatter diffraction and cathodoluminescence) in combination with secondary ion mass spectroscopy for [Ti] measurement. The most efficient [Ti] resetting mainly occurs along wetted high angle boundaries (misorientation angle >10-15°), and to a minor extend (partial resetting) along dry low angle boundaries (<10-15°). This key-study prove for the first time that pure subgrain rotation recrystallization in combination with dissolution-precipitation under retrograde condition is able to provide microstructural sites to apply the TitaniQ geothermobarometer at deformation temperatures down to 300-350 °C provided that information on pressure and Ti-activity is available.

Interpreting seismo-acoustic signals is critical for assessing and characterizing changes in volcanic vents and has implications for interpreting volcanic unrest. This is especially relevant for Stromboli volcano (Italy), an active stratovolcano with a complex plumbing system, continuous activity, and recurring paroxysms. Stromboli is known for its consistent Strombolian style of eruption, multiple active vents on its crater terrace, and for occasional structural modifications including explosive excavation and/or collapsing craters due to near-surface changes to the plumbing system. This study addresses a single localized collapse of the crater terrace, occurring in May of 2019, when one of Stromboli’s vents changed from a pronounced hornito to a pit crater, resulting in a shift in eruption style at this vent from jetting to Strombolian. The days before and after this transition were recorded with eight infrasound sensors and three seismic geophones located on the crater terrace. We investigate the seismo-acoustic timing of these signals as well as the ratio between seismic and acoustic energy to identify changes associated with eruptive signals and associated variations in location of the eruptive sources. This work highlights the effectiveness of seismo-acoustic data analysis, provides insight into Stromboli’s structural modifications, and builds a foundation for focused analysis of seismo-acoustic signals associated with Stromboli and other open-vent volcanic systems.

Canola has a prominent floral signature and requires careful consideration when selecting spectral indices for yield estimation. This study evaluated several spectral indices derived from high-resolution RGB images. A small plot (2.75m x 6m) experiment was conducted at Kernen Research Farm, Saskatoon, where canola was grown under varying row spacings and seeding rates (192 plots). The canopy reflectance was imaged during the flowering period and seed yield was obtained at physiological maturity. Indices were evaluated for accuracy in quantifying canola flowers in high-resolution RGB imagery with within-canopy shadow pixels. Digitalized flower number from the peak flowering date was used to test and validate a non-linear three-parameter asymptotic regression model to simulate canola seed yield. 70 % of the data was used to develop the model, and 30 % was used to validate the model. Model performance was tested with Pseudo-R2, r, MAE, and RMSE. HrFI (High-resolution Flowering Index) and MYI (Modified Yellowness Index) were able to accurately identify flowering pixels with the least amount of error pixel. The yield simulation model resulted in a pseudo-R2 value of 0.11 for the tested model and, a correlation of 0.91 for validation with RMSE and MAE of 343.1 and 265.3, respectively. Our results indicate that the HrFI index is a better indicator of yield potential compared to NDYI as the metric is well capable of handling within canopy shadows. Further studies are necessary to evaluate the performance of HrFI for medium resolution-UAV and satellite imagery.

High-throughput plant phenotyping is increasingly implemented in a wide array of experimentation and presents challenges both logistically and analytically. Phenotype data are often longitudinal and proper modeling of plant growth requires sophisticated modeling techniques to account for the intra-plant correlations and changing variation over time (heteroskedasticity). For this reason, plant growth is often analyzed by comparing only single time points or the start and end points for inference with no regard for the trends themselves. Single time point analysis can be sufficient for simple biological comparisons, but modeling has the potential to unlock additional insights by utilizing all the information at hand. Current plant growth modeling strategies do account for intra-plant correlations but are still limited to constant variance assumptions and therefore perform sub-optimally. Here we propose a Bayesian hierarchical approach as an alternative method for plant growth modeling by demonstrating the utility of heteroskedastic sub-model parameterizations. We show that accounting for heteroskedasticity greatly improves model accuracy and subsequent inference. Additionally, Bayesian methodologies inherently lend themselves to near real-time model updating and we propose integration with Clowder to facilitate adaptive experimental designs. We show by example the utility of Bayesian updating and how it relates to experimental decision making.

Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22–0.73) and 18 (0.23–0.87) was higher than that for day 12 (0.24–0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake “steep, cheap, and deep” ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.

For field workers around the world, wheat trials are often synonymous with wheat heads counting: a tedious but important task to measure this important yield component. Deep Learning has been a promising solution to automate the acquisition of wheat head density from a high-throughput phenotyping system, but it has been shown to be sensitive to changing acquisition conditions, also known as “domain change.” In response, an international collaboration built the “Global Wheat Head Dataset” in 2020 and 2021, a collection of 6515 images acquired during 47 different acquisition sessions in 12 countries. In addition to these datasets, two data competitions were held in 2020 (Kaggle, over 2,200 competitors) and 2021 (AIcrowd, over 400 competitors). The winning solutions are expected to be usable in plant phenotyping pipelines to robustly assess wheat spike density. We tested this hypothesis by evaluating the 2021 winning solution on an independent dataset consisting of images measured both in the field and in the image by a human, taken with the same acquisition protocol. We use triple collocation analysis to demonstrate that the predicted density appears to be more reliable than the human density measured in the field and in the image. Furthermore, we demonstrate that Global Wheat Head Dataset can be used to estimate wheat ear density from a drone.

The main cause of tsunamis is large subduction zone earthquakes with seismic magnitudes Mw > 7, but submarine volcanic processes can also generate tsunamis. At the submarine Sumisu caldera in the Izu–Bonin arc, moderate-sized earthquakes with Mw < 6 occur almost once a decade and cause meter-scale tsunamis. The source mechanism of the volcanic earthquakes is poorly understood. Here we use tsunami and seismic data for the recent 2015 event to show that abrupt uplift of the submarine caldera, with a large brittle rupture of the ring fault system due to overpressure in its magma reservoir, caused the earthquake and tsunami. This submarine trapdoor faulting mechanism can efficiently generate tsunamis due to large vertical seafloor displacements, but it inefficiently radiates long-period seismic waves. Similar seismic radiation patterns and tsunami waveforms due to repeated earthquakes indicate that continuous magma supply into the caldera induces quasi-regular trapdoor faulting. This mechanism of tsunami generation by submarine trapdoor faulting underscores the need to monitor submarine calderas for robust assessment of tsunami hazards.

Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single- and multi-variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer-wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter-model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

Quantifying phenotypes of root-root interactions would allow a greater understanding of how plants react to belowground competition through plasticity of architectural traits. Past research has shown that plants will over proliferate roots in the presence of competition, leaving less resources to allocate above ground, negatively impacting shoot growth and yields [1]. Further evidence highlights a response to neighboring plants in the root architecture of Arabidposis thaliana, as individuals concentrate root mass towards their competitors [2]. To visualize and quantify root architecture plasticity involved in these root-root interactions in real soil, we developed a modified mesocosm system. Within the mesocosm box common bean (Phaseolus vulgaris) seeds were germinated 10 inches apart from each other. Mesh screens were placed on either side of each bean, in order to capture root growth towards each other and/or away from each other. Plants were harvested at the 7-week mark, when the root archetype of each individual was developed and prominent. To harvest our boxes three sides of the mesocosm box was removed and the soil was washed away. Images of each box was taken to produce 3D models. Improvements to the mesocosm system will be made to reliably extract root traits in real soil. These experiments will shine light on an understudied section of crop science and will allow farmers and researchers a better understanding of an otherwise unseen phenomena.