We present a methodology that allows real-time simulation of the geoelectric field (GEF) spatiotemporal evolution in a given 3-D conductivity model of the Earth based on continuously augmented inducing source data. The presented concept is validated using Fennoscandia as a test region. The choice of Fennoscandia is motivated by several reasons. First, it is a high latitude region, where the GEF is expected to be particularly large. Second, there exists a 3-D ground electrical conductivity model of the region. Third, the regional magnetometer network, IMAGE, allows us to build a realistic model of the source for a given geomagnetic disturbance. Taking the 7-8 September 2017 geomagnetic storm as a space weather event, we show that real-time high-resolution 3-D modeling of the GEF is feasible and requires only a few tens of seconds.

Northern corn leaf blight (NCLB), caused by the fungus Setosphaeira turcica, is one of the most devastating corn diseases in the U.S. and has reduced Indiana yields for the last decade. NCLB can cause significant yield loss if conditions are favorable for early disease onset near tassel/silk (VT/R1) growth stages. Therefore, early detection and timely disease management are essential to mitigate yield loss and optimize fungicide applications. Remote sensing technologies have broad applications in plant phenotyping areas, but they present specific challenges in the early detection of diseases, particularly for diseases where lesions first appear in the lower crop canopy. While laboratories can provide more accurate results than remote sensing, their throughput is limited. To achieve higher throughput and more precise measurements for improved early disease detection, a portable digital microscope prototype has been developed. Distinct from other portable microscopes, this device can detect pathogens and directly monitor their growth on live plant tissue due to its 1 nm spatial resolution. Moreover, it is built with a confocal configuration to capture high-resolution reflective images, thereby outperforming traditional microscopes in throughput. This feature also permits repetitive non-destructible measurements on a single leaf and accommodates leaves of varying thicknesses. By analyzing time-series images of infected leaves, this portable digital microscope has proven its ability to detect spores and germ tubes of the pathogen before symptoms are visibly apparent. It has the potential to effectively bridge the gap between precision and throughput, providing critical insights into disease management.

Key Points:Lagrangian-based HYSPLIT modelling system used to estimate volcanic ash particle trajectories.HYSPLIT simulation took place before and after the massive eruption on 15th January 2022 (termed as pre-caldera and post-caldera respectively in Section 5)Volcanic ash particle deposition and volcanic ash particle position simulated using HYSPLIT for the HTHH submarine volcano massive eruption event.AbstractVolcano-seismic signals such as long-period (LP) events and tremors are important indicators for volcanic activity and unrest. Explosive volcanic eruptions are stunning phenomena that influence the Earth’s natural systems and climate in a variety of ways. This paper discusses the mid-week January 2022 eruption of the HTHH submarine volcano, especially on 15th January an event with many impacts in the region (dynamic, chemical, climate breakdown). Given the potential for a volcanic eruption to affect climate, the oceanic system, or climate variability, consistent and understandable modelling of these exceptional events is critical.The main objective was to determine the volcanic effects in our atmospheric boundary layer (ABL) during the multiple eruptive events occurred on January 2022 at HTHH. Our discussion also contributes to understanding the underlying Earth system dynamics triggered by cataclysmic volcanic eruptions. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model system developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory was used to deliberate the effects caused by the multiple eruptions of HTHH on mid-week of January 2022. Our modelling results include model trajectories at different frequency levels, volcanic ash deposition and ash particle position from the series of multiple eruption events of submarine volcano HTHH in the mid-week of January 2022.

Thawing of permanently frozen ground (permafrost) has increased in recent decades with negative implications for human and non-human adaptation to climate change. Impacts include reduced ground stability, increased transportation risk, and changes in water availability. Direct measurements of permafrost active layer thickness (the depth of thawed ground overlying permafrost) are sparse. Measurements currently exist for a few hundred sites located primarily in the Northern Hemisphere supported by the Circumpolar Active Layer Monitoring (CALM) Program. The sparsity of direct active layer thickness measurements limits broad-scale understanding of changes in permafrost thaw and confidence in future projections. To address the sparsity of direct active layer thickness measurements, we developed a method to estimate active layer thickness change from streamflow measurements, which integrate processes over broad spatial areas and are more common than point-scale active layer thickness measurements. The method uses classical principles of hydraulic groundwater theory and nonlinear baseflow recession analysis, which sets it apart from prior methods based on linear recession analysis. The method is applied to catchments in the continuous and discontinuous permafrost zone of the North American Arctic containing co-located streamflow and CALM active layer thickness measurements. We find good agreement in the magnitude and direction of measured and predicted active layer thickness trends. This suggests that regional-scale estimates of active layer thickness change can be obtained from streamflow measurements, which may open the door to retrospective estimation of active layer thickness change in data sparse Arctic regions with short, sporadic, or even nonexistent ground-based active layer measurements.

Ziling Chen1, Jialei Wang2, Xuan Li1, Jian Jin1

Plant reproduction is sensitive to heat stress. Pollen tube growth can be accelerated or arrested by high temperatures, leading to unstable tubes, failed sperm cell delivery, and ultimately crop yield loss. Pollen growth dynamics have historically been observed on the scale of individual pollen grains, but there are only a few studies surveying pollen populations across genotypes and environmental conditions. Here we describe a phenotyping system that quantifies tomato pollen characteristics on a large scale and under varied heat stress conditions. In this system, we combined high-throughput bright-field microscopy with automated object detection and tracking to investigate the lives of growing pollen tubes. We used this method to survey pollen from a diverse panel of 220 tomato and close wild relative accessions under different temperatures. This method can be readily adapted to pollen from difference species, providing a rapid way to characterize heat stress responses and molecular functions in flowering plants.

Dani C. Gafford1, Jaime Barros1

Volcanic eruptions are potentially hazardous natural events. Understanding how magma accumulates, migrates, and erupts is important to understanding and, eventually, predicting volcanic eruptions. However, the variation in the scale of volcanoes, co-occurrence of earthquakes, and the duration of the eruption makes understanding these events difficult. Ambient noise interferometry is becoming an increasingly more popular tool to study and monitor active volcanoes. We use this method to characterize the variations of subsurface seismic velocities associated with different stages of the eruption process at the Great Sitkin Volcano in the central Aleutian volcanic arc. This volcano initially erupted in May 2021 with elevated seismicity and gas release, followed by the formation of a new lava dome starting July 2021. The volcano had an increase in seismicity in February 2020 but without any eruption activity reported. Measuring the variation of seismic velocities from August 2019 to March 2022, we observe a local decrease in velocity leading up to the eruption and an increase in velocity following the emplacement of the lava dome. We do not observe any velocity variations preceding the non-eruptive increase of seismic activity in February 2020. Despite its remote location and relatively small scale, the findings of this study at the Great Sitkin volcano have significant implications for understanding volcanism and the development and prediction of volcanic eruptions in general.

A document by Zahra Zali. Click on the document to view its contents.

Evaluation of the performance of hydrologic and hydraulic models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as the Nash Sutcliffe efficiency (NSE), the Kling Gupta efficiency (KGE), and the coefficient of determination (R2), which are widely used in the evaluation of model performance, an evaluation framework that incorporates multiple criteria and based on the generalized likelihood uncertainty estimation (GLUE) is proposed to demonstrate the uncertainty in the evaluation criteria and hence to quantify the overall uncertainty of flood models in a comprehensive way. This framework is applied to the one-dimensional HEC-RAS models of six reaches located in States of Indiana and Texas of the United States to quantify the uncertainty associated with the channel roughness and upstream flow input. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high-flow scenarios, and various types of measurement errors (white noise, positive bias, and negative bias) in observations on the evaluation metrics are investigated by using the bootstrapping method and Monte Carlo simulations. The results show that the model performances based on the uniform and normal priors are comparable. The distributions of all the evaluation metrics in the framework are significantly different for the flood model under different high-flow scenarios, and it further indicates that the metrics are essentially random statistical variables. Additionally, the white-noise error in observations has the least impact on the metrics, while the positive and the negative biases would have opposite impacts, which depends on whether the model overestimated or underestimated the hydrologic variable.

Water scarcity profoundly affects crop growth in rain-fed regions, including the Pacific Northwest (PNW) of the USA. While unmanned aerial vehicles (UAVs) are integral for crop monitoring in breeding programs, their use is resource-intensive and necessitates pilot presence in the field. Alternatively, Internet of Things (IoT)-based sensor systems offer continuous, remote, and real-time monitoring, but their data integrity requires validation for field applications. This study developed a Raspberry Pi-based sensor system (AGIcam+) and compared its efficacy with UAV in discerning crop responses to drought conditions across various wheat varieties in the PNW region. Multispectral and thermal data were collected across wheat trials (Winter 2023; Spring 2022, 2023) at crucial growth stages – preheading, heading, and post-heading – under varied drought stress conditions. Key vegetation indices and temperature measurements were extracted for a comparative drought performance analysis. Results indicate significant correlations between AGIcam+ and UAV data, more pronounced during the heading and post-heading stages. Pearson’s correlation coefficients for the average normalized difference vegetation index (NDVI) and the temperature data exhibited ranges of 0.81-0.88 and 0.77-0.96 (P < 0.01), respectively, across all trials during the heading stages. Yield prediction models using random forest regression analysis from both systems’ data underscored AGIcam+’s accuracy in yield estimation, demonstrating performance comparable to UAV, as evidenced in the Spring 2023 trial ( AGIcam+: R2 = 0.85, RMSE = 796.9 kg/ha; UAV: R2 = 0.84, RMSE = 825.0 kg/ha). These findings underscore AGIcam+ as a resource-efficient crop monitoring alternative, effectively capturing responses to environmental conditions and facilitating accurate yield predictions under drought stress.

Environmental stressors play a major role in determining plant phenotypic traits over the growing season. Nitrogen, a critical nutrient for plant growth, imposes serious stresses when applied in insufficient or excessive quantities in crop fields, which can directly reduce yield and the grower's profits. Due to significant variations in developmental rates and nitrogen needs across rice genotypes and environments, field phenotyping can become a challenging task. Non-linear manifold learning approaches were evaluated to account for variation in developmental rate using multispectral, multitemporal imagery acquired by an unmanned aerial vehicle (UAV). Imagery (13-16 flyovers per plot) and ground-truth data (including biomass nitrogen content, leaf chlorophyll content, and yield) were collected on 2,345 rice research plots during the 2021 and 2022 growing seasons. To increase the size of the training set, we also evaluated inclusion of similarly sized tiles sampled from commercial row-rice fields imaged between 2019 and 2022. We compared several supervised machine learning approaches with unsupervised strategies based on manifold learning to evaluate the hypothesis that model architectures which account for variation in developmental trajectories improve prediction of yield variation in response to nitrogen addition. This work will contribute to improved nitrogen rate recommendations using UAV-acquired imagery. Optimized nitrogen prescriptions ensure satisfying the plants' nitrogen needs and contribute to reduced environment impacts and production costs.

The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.

Animal behaviour such as dispersal and migration ensure their survival in the landscape. It has been established in the past few decades that wildlife conservation and study of their movement in the wilderness is vital for sustainable ecosystem. Thus, identification of regions having high movement permeability for planning and maintenance of functional wildlife corridors has turn out to be a fundamental requirement for habitat management. This study emphases on movement of big cats-Bengal Tiger (Panthera tigris tigris) and Leopard (Panthera pardus fusca) in the protected area of Rajaji National Park situated in Uttarakhand State of India. The National park is a designated tiger reserve with large amount of tigers and leopards at its disposal. Here, Circuitscape was used to generate connectivity map of the study area. The results were validated using occurrence points downloaded from GBIF. The habitat suitability and resistance of the landscape was estimated based on literature review and expert opinion survey. Since, both the species have comparable ecological niche, similar habitat parameters were used for generation of resistance map of the species. Occurrence points for the species were downloaded from GBIF. 60% of the points were used as nodes or focal points where species presence is recorded whereas 40% of the points were used in validation of the connectivity paths. Results depicts the current density map of the study area highlighting areas with high connectivity for the species.

COVID-19’s impact on society and our daily habits has been unprecedented. With a decrease in vehicular traffic and industrial production, a decrease in local emissions was expected to occur. In order to capture any trends in ambient trace gas concentrations, approximately one thousand whole air samples were collected in intervals across the United States from April to July 2020 as part of the NASA Student Airborne Research Program (SARP). These samples were then analyzed by the UCI Rowland-Blake Lab using multi-column gas chromatography for over one hundred unique trace gases, including methane, non-methane hydrocarbons, and halocarbons, as described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders in most states while samples collected later in the spring and early summer reflect the easing of these measures and initial state reopenings. Overall trends in emissions over time in select metropolitan areas will be discussed and compared to trends observed across the entire United States.

Crop breeding relies on the numbers game. The higher the number of locations and entries evaluated, the higher the probability of developing superior cultivars. One major challenge facing breeders of perennial cool season forage crops is the number of biomass harvests per season. In regions with mild winters, alfalfa is harvested every four weeks, six to seven times a year. This creates an operational bottleneck limiting the number of entries and testing locations. Substantial investments were made in the development of automated solutions for precision Ag for row crops. The adoption of these platforms to forage crops rests on their accuracy in estimating biomass yield and cost-effectiveness. This work focused on evaluating the sensitivity of popular unmanned aerial vehicles (UAVs) and imaging strategies for optimal real-time biomass estimates in perennial forage crops. Experimental plots consisting of single plants, row plots, and sward plots were used for a hybrid data collection approach including direct measurements and remote sensing. UAV platforms equipped with a 42-megapixel RGB camera (Sony Alpha 7Rii), a five-band multispectral system (MicaSense RedEdge MX), a hyperspectral sensor (Resonon-Pika L), and a LiDAR (LiDARUSA Revolution 120) were tested. Images were used to generate 3D canopy models of vegetation in the field and to compute morphometric and spectral indices descriptive of vegetation coverage, health and vigor. Harvested biomass yield was used to validate the values derived from UAVs. Preliminary results suggest that the simple red-green vegetation index may be sufficient to give a reliable estimate of biomass yield.

The earliest human migration from Sunda (South-East Asian archipelago) to Sahul (Australia and New Guinea) is still heavily debated with proposed timings between c.65-45kaBP depending on the evidence base and interpretation of the data. As part of the EU funded ACROSS project, focused on the mode and route of early migration in to SAHUL, we are undertaking an integrated interpretative study of the evolving submerged landscapes for the Late Pleistocene of the NW Australian Shelf. Oil and gas industry 3D and 2D seismic data, with some core/borehole data, are being used to determine lowstand palaeo-environments and shoreline positions. This information is informing modelling of ocean tide and current patterns that may have been influenced. The seismic is being interpreted supplemented by using time-slices on relative impedance inverted post-stack data. Layer stripping, seismic geomorphology, sequence boundary and depth analysis are being applied to datasets in the Bonaparte Basin, Kimberley Shelf and Arafura regions of Australia’s North-West Shelf area. Interpretation of the seismic data is constrained by dated stratigraphy in shallow cores with lower bounds determined from oil/gas well bores. MIS stages 1-4 are identified, however, the seismic response is a composite of time periods due to varying sedimentation rates, non-depositional hiatuses and minimal vertical seismic travel time covering this interval which limits the analysis to the top 50ms TWT (c. 40-45 m) of events below the seabed. This paper reviews the workflows that have been developed to maximise the fine scale detail that can be recovered for a range of terrestrial and marine environments. Procedures include inverse-Q, impedance inversion, spectral decomposition and time-slicing relative to seabed. High resolution 2D seismic data is also being used to augment and inform the interpretation of the conventional oil/gas 3D seismic data. Data examples will be presented showing the geomorphological characteristics (river channels, avulsions, levees, drainage channels, dunes and near shore carbonate reefs) of the lowstand and transgressive landscapes during this period. The palaeo-reconstructions are now being developed from the interpreted seismic geomorphology for the specific consideration of human seaborne travel.

Water stress mapping in crops and its spatial disparity study at field scale is important for precise management of irrigation. Results obtained from conventional airborne practice (balloons, airplanes, and satellites) are less acceptable for timely irrigation management due to lack in spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with multispectral (MS) and thermal cameras with higher spectral and temporal resolutions can be used as a promising tool for preparing water stress maps under different water deficit conditions. In this study, Water Deficit Index (WDI) maps are generated at different days after sowing (DAS) in wheat crops under three different water conditions (WI (well water), WS1(irrigation at 5 days’ interval), and WS2 (irrigation at 11 days’ interval)) using the concept of Vegetation Index Trapezoid (VIT) using UAV based thermal and MS imageries. The UAV is flown at 60m altitude during the Rabi season 2018-19. After pre-processing of images in Pix4dMapper, nine vegetation indices are calculated from MS images and one of the indices, Normalized Green Red Difference Index (NGRDI) is selected based on the higher correlation with ground truth data (R2 greater than 0.5) and visual interpretation according to the real field condition to construct the VIT. Vegetation index and temperature values are calculated for four points of VIT by using four boundary conditions such as bare soil with (1) dry and (2) wet conditions, and full vegetation with (3) well-watered and (4) water stress conditions. By using the ArcGIS, geo-referencing of thermal images with respect to MS images is done to get the exact overlap of both images, and resampling of thermal and MS images are also carried out to get the same pixel size. WDI values are estimated using VIT of the surface-air temperature difference and NGRDI, and WDI maps are generated from the UAV-based thermal and MS imageries for potential detection of crop water stress. The conventional Crop Water Stress Index (CWSI) which is solely based on the crop canopy temperature is outperformed by the WDI, which is integration of composite land surface temperature (LST) and degree of greenness, and could be effective enough for irrigation water management. Keywords: UAV, Multispectral and Thermal imageries, NGRDI, WDI, and Wheat crop.