The Arctic Ocean's Wandel Sea is the easternmost sector of the Last Ice Area, where thick, old sea ice is expected to endure longer than elsewhere. Nevertheless, in August 2020 the area experienced record-low sea ice concentration. Here we use satellite data and sea ice model experiments to determine what caused this record sea ice minimum. In our simulations there was a multi-year sea-ice thinning trend due to climate change. Natural climate variability expressed as wind-forced ice advection and subsequent melt added to this trend. In spring 2020, the Wandel Sea had a mixture of both thin and---unusual for recent years---thick ice, but this thick ice was not sufficiently widespread to prevent the summer sea ice concentration minimum. With continued thinning, more frequent low summer sea ice events are expected. We suggest that the Last Ice Area, an important refuge for ice-dependent species, is less resilient to warming than previously thought.

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.

Over 270 major dams have been constructed in the Southeastern United States (SEUS) during the past century, changing natural flow patterns and affecting stream temperatures. Projected increases in air temperature combined with changes in precipitation may result in water scarcity and affect maximum stream temperatures during the summer for some regions in the SEUS. Currently existing reservoirs mitigate water shortages during drought by releasing more water but reducing residence time, the ratio of reservoir volume to inflow. Regulating stream temperature in the summer can be done by either increasing residence time or releasing more water. In this study, we investigate the extent to which the current reservoir infrastructure can be used to mitigate the impacts of climate change under current reservoir regulations as well as the range of operating rules that could minimize climate change impacts on both streamflow and river temperature. We use the Variable Infiltration Capacity (VIC) hydrological model to simulate runoff, which is then used as input to a large-scale river routing-reservoir model (MOSART-WM) to simulate reservoir operations and produce regulated streamflow. VIC and MOSART-WM outputs are then used as input to a stream temperature model that accounts for thermal stratification in reservoirs (RBM-res). Climate change projections are based on two representative concentration pathways (RCPs) and multiple global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We compare modeled changes with those from a model implementation that does not include any reservoirs and which therefore lacks any flow regulation (VIC->MOSART-RBM) to evaluate the resilience of current reservoir infrastructures. We also evaluate different reservoir operating rules (residence time versus low flow mitigation) to investigate the extent to which the current reservoir system can be used to mitigate the impacts of climate changes on both streamflow and stream temperature.

Nitrogen fertilizers are one of the top expenses for corn farmers in North America, and the highest cost of any input over a growing season. The success of developing better strategies for nitrogen-use efficiency, such as improved varieties and biologics, depends on an efficient way of measuring in-planta nitrogen content. We are testing a reflectance-based UAV hyperspectral approach and a transmittance-based hyperspectral handheld called LeafSpec to estimate corn nitrogen from hyperspectral images. The UAV approach is high throughput but with relatively low spatial resolution and has high susceptibility to environmental factors. The LeafSpec is low throughput but with high spatial resolution and is not significantly affected by environmental factors. Also, the LeafSpec can account for the nitrogen inside the corn leaves by using transmittance, whereas the UAV can only see surface effects from reflectance. We are testing well-established machine learning and state-of-the-art deep learning models for both approaches. This presentation will share our learnings from testing UAV and LeafSpec as two hyperspectral imaging approaches to estimate corn plant nitrogen from hyperspectral images regarding potential use-cases for each approach, considering precision, ease of use, throughput, and potential for further development.

Coal combustion aerosol particles contribute to the concentrations of ice-nucleating particles (INPs) in the atmosphere. Especially, immersion freezing can be considered as one of the most important mechanisms for INP formation in supercooled tropospheric clouds that exist at temperatures between 0°C and -38°C. The U.S. contains more than 550 operating coal-burning plants consuming 7.2 x 10^8 metric tons of coal (in 2016) to generate a total annual electricity of >2 billion MW-h, resulting in the emission of at least 4.9 x 10^5 metric tons of PM10 (particulate matter smaller than 10 µm in diameter). In Texas alone, 19 combustion plants generate 0.15 billion MW-h electricity and >2.4 x 10^4 metric tons of PM10. Here we present the immersion freezing behavior of combustion fly ash and bottom ash particles collected in the Texas Panhandle region. Two types of particulate samples, namely <45 µm sieved bottom ash (B_Ash_TX_PH) and <45 µm sieved fly ash (F_Ash_TX_PH), were prepared. Afterwards, their immersion freezing abilities were measured using the Cryogenic Refrigerator Applied to Freezing Test (CRAFT) system covering the heterogeneous freezing temperature down to -30 °C. The results were generated and are reported through two metrics, frozen fraction, ffrozen(T), and ice nucleation active site density per unit mass, nm(T) as a function of temperature. Our preliminary results show that an onset increase in ffrozen(T) for B_Ash_TX_PH (ffrozen) occurred as high as at -15°C, whereas the onset for F_Ash_TX_PH is at -18°C. Secondly, B_Ash_TX_PH exhibited a higher nm(-20 °C) of 10^5 g^-1 than that of F_Ash_TX_PH ( 5 x 10^3 g^-1). On the other hand, previous studies on different combustion ash samples have reported that the opposite trend (i.e., ice nucleation efficiency of fly ash is greater than that of bottom ash; Grawe et al., 2016, ACP; Umo et al., 2015, ACP). We will discuss possible reasons for the observed differences. In addition, the results of complementary physico-chemical analyses via X-ray diffraction technique, Raman microscopy and scanning electron microscopy on both ash types will also be presented to relate the crystallographic and chemical properties to their ice nucleation abilities.

Melt inclusion analysis used to constrain and compare parental melt compositions of the nakhlite and chassignite martian meteorites.

We used the process-based and tracer-aided ecohydrological model EcH2O-iso to assess the effects of vegetation cover on water balance partitioning and associated flux ages under temperate beech forest (F) and grassland (G) in Northern Germany. The model was tuned on the basis of a multi-criteria calibration against an unusually rich measured data set from a long-term monitoring site.. The calibration incorporates metrics of the energy balance, hydrological function and biomass accumulation. It resulted in good efficiency statistics for simulations of surface energy exchange, soil water content, transpiration and biomass production. The model simulations showed that the forest “used” more water than the grassland; from 620mm of average annual precipitation, losses were higher through interception (29% under F, 16% for G) and combined soil evaporation and transpiration (59% F, 47% G). As a result, groundwater recharge was greatly enhanced under grassland at 37% of precipitation compared with12% for forest. The model allowed us to track the ages of water in the different storage compartments and fluxes.In the shallow soil horizons, the average ages of soil water fluxes and evaporation were similar in both plots (∼1.5month), though transpiration and groundwater recharge were older under forest (∼6 months compared with∼3months for transpiration and∼12 months compared with∼10 months for groundwater). Flux tracking with Cl tracers provided independent support for the modelling results, though also highlighted effects of uncertainties in forest partitioning of evaporation and transpiration. This underlines the potential for tracer aided ecohydrological models in land use change studies. By tracking storage – flux – age interactions under different land covers, the effects on water partitioning and age distributions can be quantified and the implications for climate change assessed.Better conceptualisation of soil water mixing processes, and improved calibration data on leaf area index and root distribution appear obvious respective modelling and data needs for improved model results.

A new soil moisture index for monitoring drought occurrence and intensity is presented. The index is based on the integration of different remote sensing products and in situ observations. Due to a shortage of precipitation, droughts reduce vegetation productivity, and thus, aggravate the impact of moisture stress on pastureland. The spatial distribution of soil moisture index with high-resolution images in Mongolia is still being one of the essential goals in the remote sensing and rangeland community. Specifically, we examined a new composite Gobi soil moisture index (GI) based on the combination of Ocean Salinity (SMOS) Soil Moisture, several products from the MODIS satellite, and in situ Soil Moisture (SM) observations. A multiple linear regression method was used for the estimation of GI soil moisture index. The former includes the surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission, the Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST), normalized difference vegetation index (NDVI), potential evapotranspiration (PET). The latter includes a standardized precipitation index (SPI) from in-situ data. The validation of the approach is based on the relationship between SPI and in-situ soil moisture (SM) observations, and their comparison to remote sensing (RS) – derived indices. The results show that the correlation was statistically significant between GI and in-situ SM observations from the meteorological stations at 10 – 15 cm depths (p < 0.0001). The correlation between GI and SPI, as represented by the correlation coefficient (r) was 0.64. The GI empirical equations that utilize at least three key atmospheric variables are (a) NDVI, (b) land surface temperature, and (c) potential evapotranspiration. The established new GI soil moisture index was retrieved at the 1 km spatial resolution for Southwest Mongolia from 2000 to 2018, and their two summer months (July, August) were used for monitoring drought and vegetation response to the varying soil/climatic conditions. Now, based on the assessment of drought severity, the new soil moisture index allowed us to assess a large-scale spatial coherence of droughts across the Southwestern part of Mongolia.

Variability in precipitation frequency, intensity and duration deeply affect vegetation and erosion yields. Under climate change scenarios, alterations in precipitation are expected but it is not well understood how it could affect erosion rates and what is the role of vegetation on landscape geomorphic response. Traditional erosion models normally include basic representations of vegetation that do not account for the dynamic character of biomass variability and feedbacks with hydrological and erosion/deposition processes. Hence, using a new modelling frameworks that account for the effect of varying vegetation cover on erosion, and that includes climate change scenarios is needed. Here we use a new model: COPLAS, a tool that couples a Landform Evolution Model with dynamic vegetation and carbon pools modules to investigate the response of landscapes to climate change. The vegetation module includes a coupled photosynthesis-stomatal conductance representation that responds to climatic data inputs as temperature, CO2 concentration and water availability. We use the model to simulate the erosional and geomorphic responses of dynamic vegetation in Howard Springs (Australia) to predicted changes in daily precipitation under future CO2 concentrations of about 940 ppm. The model was calibrated using Ozflux site historical data. Catchment scale simulations were run for a period of 100 years for three scenarios (bare soil, constant and dynamic vegetation) using a daily time step. We found that, for our study case, bare soil produces on average 139% more erosion than the constant vegetation case and 124% more than the dynamic vegetation case. Moreover, an increase in precipitation of around 23% induces an increase of 25%, 35% and 43% in the erosion rates for dynamic vegetation, constant vegetation and bare soil respectively, while a decrease in 26% reduces it in 36%, 60% and 59%. This could be explained by the nonlinear relation between erosion and vegetation (higher rainfall induces higher erosion potential which can be counteracted by an increase in vegetation cover leading to a decrease in soil erodibilty). This finding highlights the importance of considering the dynamic character of vegetation in order to understand the nonlinear relations between fluvial erosion and vegetation cover.

River systems represent important drivers of carbon loading, utilization and storage. However, underlying controls of hydrological transport and biological degradation on fluvial dissolved organic carbon (DOC) have yet to be revealed. Here, we explored spatiotemporal variability of DOC concentrations, components and sources, as well as its biodegradability in a headwater tributary of the Yangtze. We found that temporal rainfall stimulated terrestrial inputs and increased terrigenous DOC abundance. Hydrological transport was accompanied by biological generation and utilization of DOC, resulting in reduced labile components and accumulated recalcitrant components from tributaries to the main stem. Biodegradable DOC (BDOC) notably responded to temperature gradients over a 56-day laboratory incubation. Riverine DOC component, molecular weight and source highly predicted its biodegradation. Particularly, partial refractory (ultraviolet humic-like) fractions contributed to biological degradation of DOC, which was incompletely degraded from high-molecular to low-molecular weight compounds. The findings hope to supplement a new understanding of carbon fate under global change.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov) and Solar Data Analysis Center (SDAC https://umbra.nascom.nasa.gov/), as the NASA Heliophysics active final archives, will be preserving and distributing the data from Parker Solar Probe. Working in cooperation with current operating missions and the heliophysics community, SPDF ingests, preserves and serves a wide range of past and current public science-quality data from the ionosphere into the furthest reach of deep-space exploration. SPDF has been working with the Parker Solar Probe mission in preparation for archiving and serving its in-situ data starting 2019 Nov 12, and also has arrangements to serve in-situ data from Solar Orbiter when those data become public. SPDF will facilitate scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of Parker Solar Probe mission. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Helios, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, Geotail, Polar, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and the OMNIweb near-Earth solar wind plasma and magnetic field database.

UNAVCO internships provide unique opportunities for students from underrepresented populations to develop their technical and communication skills while aiming to broaden participation in the Earth sciences. UNAVCO, a non-profit that operates the National Science Foundation-funded GAGE facility, has the opportunity to facilitate multiple internship programs, engaging students from across the United States. Prior to 2020, all internship programs were in-residence in Boulder, Colorado. Internship programs included project work and in-person professional development as well as cohort building, multi-level mentoring, and professional networking in the science-rich Boulder area. In light of the COVID-19 pandemic and ‘safer-at-home’ directives nationwide, we successfully converted our three internship programs to a remote format with fully online programming. We present our strategies for successfully planning, managing, and facilitating three online internship programs with 12 student participants ranging from community college to undergraduates to graduate students who all participated from their home locations. We also share our considerations for converting our internship programming to an online format, expanding our interns’ mentoring network and opportunities, and increasing our programs’ flexibility for interns who were personally impacted by national events. We present our process of identifying and engaging online learning experts and working collaboratively with all stakeholders to ensure each of our internships continued to be of the highest quality and our student experiences were not diminished. By offering the full length and robustness of our programs through a remote platform, we kept our promises of education, career advancement, monetary compensation, and community connection to our full 2020 intern cohort. In addition to offering geoscience projects and research experience to each intern, we implemented a robust suite of synchronous, online professional development courses that occurred weekly, including technical writing workshops, Geoscience Career Circles, and Communications Seminars focused on soft skill development. We also describe the support and training that we offered project mentors as they virtually guided students through successfully completing rigorous science projects and a suite of project deliverables including scientific posters, oral presentations, and technical manuscripts.

Since we discovered the newly morphological transpolar arc (TPA), whose nightside end got distorted toward pre- or post-midnight, identified as “nightside distorted TPAs”, their fundamental characteristics have been revealed based on investigations of the space-borne auroral imager data and corresponding solar wind conditions. Nightside distorted TPAs had two types; “J”- and “L”-shaped TPAs, and their locations of appearance (dawn or duskside of the polar cap) were governed by the polarity of the By component of the Interplanetary Magnetic Field (IMF). Furthermore, we found that the nightside distorted TPAs have antisymmetric morphologies in the Northern and Southern hemispheres, also depending on the IMF-By orientation. In this presentation, we show that that the electric currents flowing aligned to the magnetic field lines which connect between the magnetotail and the ionosphere, that is, Field-aligned currents (FACs) play an essential role in the formations of the “J”- and “L”-shaped TPAs. They are induced by significant plasma flow velocity difference (plasma flow shear) between the fast plasma flows associated with nightside magnetic reconnection and slower background plasma flows in the magnetotail. The current vortex structures with the counterclockwise rotation are also clearly seen in the ionospheric current vectors derived from fluctuations of the geomagnetic field measured at the ground observatories beneath and in close proximity of the growth regions of the nightside distorted TPA. This result suggests that the FACs were flowing out of the ionosphere toward the magnetotail (upward FACs) near the TPA. Furthermore, based on the geomagnetic field variations and the SuperDARN HF radar data, we obtained evidence in which the locations of magnetotail magnetic reconnection, which persisted even during northward IMF-Bz intervals, that is, the TPA durations, retreated further down tail as the TPA grew to the dayside. Taking into account these observational results, we finally show a model to illustrate the nightside distorted TPA (particularly, “L”-shaped TPA) formation.

The aim of this study is to determine the hydrochemical properties of water resources in the Seyfe Basin and to understand processes involving water chemistry using statistical techniques such as principal component analysis (PCA) and cluster analysis. Seyfe Lake closed basin is located west of Kirsehir (Turkey) in the Central Anatolia. Considering the importance of the Seyfe Lake Basin for its water resources potential and due to its fragile ecosystem, a detailed hydrogeological characterization of the basin has been undertaken. In order to characterize hydrogeological system accurately, water samples were collected from springs, wells, drainage channels during September and December 2019 field campaigns from the Seyfe basin. The temperature, pH, total dissolved solids (TDS) and specific electrical conductance (EC25°C) were measured at the field and spatio-temporal changes of major ions are evaluated. The waters have neutral to slightly alkaline character, and most of the water samples are saturated with calcite, dolomite and aragonite minerals. According to the results obtained from Gibbs, Piper plot, and PCA, the water-rock interaction takes place without any remarkable ion exchange reactions. Water facies identified with the Piper diagram; supports this view. Water types are determined as Ca-HCO3 for springs; Na-HCO3 for wells; Na-SO4 for drainage channel in the dry season and Na-Cl in the rainy season. In Ihe study area; ionic compositions of the springs are not affected by seasonal changes. The high sulfate concentration is probably related to the weathering of evaporitic rocks in the basin. The waters are grouped according to the results, and the difference observed within the groups are related to the lithology of aquifers and flow path of the waters. KEYWORDS: Hydrochemistry, Major ions, Kırsehir, Central Anatolia, Seyfe Closed Basin, Spatio-temporal Changes.

Modeling future glacier changes on regional or global scales is challenging due to a scarcity of data for calibrating model parameters. This makes it difficult to derive accurate model parameters and also to convey the model uncertainty associated with those parameters. Previous global-scale models have typically optimized a single glacier or region specific set of model parameters to project global-scale glacier mass changes. As part of the development of a new open source global-scale glacier evolution model this research implements a Markov Chain Monte Carlo method to address this problem. We use geodetic mass-balance observations to determine probability distributions of a set of model parameters at each glacier. We then form an ensemble of model simulations by sampling parameters from these distributions. Using an ensemble allows us to create a range of mass balance changes for each glacier and quantify the model uncertainty. Applying this calibration technique, we use temperature and precipitation projections of one global climate model to model the mass changes until year 2100 of thousands of glaciers in High Mountain Asia. By using climate projections forced by multiple emission scenarios, we compare the uncertainties in changing climate caused by possible emission scenarios with the uncertainties in the glacier evolution model, and evaluate the results of different climate scenarios on glacier evolution.

Most climate change impacts are linked to terrestrial vegetation productivity, carbon stocks and land use change. Changes in land use and climate drive the dynamics of terrestrial carbon cycle. These carbon cycle dynamics operate at different spatial and temporal scales. Quantification of the spatial and temporal variability of carbon flux has been challenging because land-atmosphere-carbon exchange is influenced by many factors, including but not limited to, land use change and climate change and variability. The study of terrestrial carbon cycle, mainly gross primary product (GPP), net ecosystem exchange (NEE), soil organic carbon (SOC) and ecosystem respiration (Re) and their interactions with land use and climate change, are critical to understanding the terrestrial ecosystem. The main objective of this study was to examine the interactions among land use, climate change and terrestrial carbon cycling in the state of Texas using satellite measurements. We studied GPP, NEE, Re and SOC distributions for five selected major land covers and all ten climate zones in Texas using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were compared with observed CO2 flux data measured at EC flux site on Prairie View A&M University Research Farm. Results showed the same land cover in different climate zones has significantly different carbon sequestration potentials. For example, cropland of the humid climate zone has higher (-228 g C/m2) carbon sequestration potentials than the semiarid climate zone (-36 g C/m2). Also, shrub land in the humid zone and in the semiarid zone showed high (-120 g C/m2) and low (-36 g C/m2) potentials of carbon sequestration, respectively, in the state. Overall, the analyses indicate CO2 storage and exchange respond differently to various land covers, and environments due to differences in water availability, root distribution and soil properties.

This project aims to quantify the resiliency of prairie ecosystems in the U.S. Pacific Northwest (PNW) to climate change. Prairies in this region sustain over one million beef cows, and cow­-calf production costs are expected to increase to offset warming-induced plant productivity loss. We investigated the above- and belowground effects of experimental warming in prairie ecosystems by assessing biogeochemical controls on and patterns of asymbiotic nitrogen fixation (ANF), plant species diversity, and legume cover to address a major challenge for sustainable agriculture in the region. We hypothesize that the effect of warming on prairie functional diversity increases soil asymbiotic nitrogen inputs by decreasing legume cover and soil nitrogen availability. We quantified the effects of decadal warming stress (+2.5ºC) on soil biogeochemical properties and plant species and functional diversity during fall and spring seasons in three sites along a 520­km latitudinal gradient—from central Washington to southern Oregon—representing a drought severity gradient. At each site, we collected composite soil samples from five co-­located prairie plots under control (ambient) and warming conditions. We incubated these soils using 15N-labeled dinitrogen (15N2), and quantified total soil carbon­, total and available nitrogen, and available phosphorus and iron pools to better understand the underlying mechanisms governing warming-­induced changes in ANF. We used a point intercept technique to survey plot-level plant community composition and calculate Shannon’s diversity index and percent cover of legumes (members of Fabaceae according to the Integrated Taxonomic Information System). Warming significantly decreased plant species diversity which also decreased along the drought severity gradient. Legume cover significantly increased from 3.1% in the north to 9.2% in the south. ANF response to warming varied by season and site, where rates increased with the drought severity gradient in the fall but decreased during the spring. Total soil inorganic nitrogen availability was the strongest predictor of ANF response to warming in the spring but not in the fall. Our study highlights the importance of using soil-plant-atmosphere interactions to assess prairie ecosystem resilience to climate change in the PNW.

A document by Valerio Filice. Click on the document to view its contents.