In Utah, significant agricultural water demand (75% of available water) combined with arid climate and population growth puts pressure on existing water resources. Also, Utah climate is very variable and strongly correlated with topographic and land-surface contrasts, with prevailing geographical weather patterns. Agricultural water management can vary depending on climatic and landscape conditions, hindering its assessment. To address this, an implemented remote sensing-based water balance will give an insight into agricultural non-consumptive water use, and performance metrics will be presented to look for opportunities of agricultural water diversion reduction under diverse Utah geographies and Irrigation Canal Companies (ICCs). In this study, as part of the USDA-sponsored Secure Water Future project, historical-remote-sensing data (OpenET and gridMET) and Geographic Information Systems (GIS) data were inputs to the water balance model at ICC scale located in three major basins (Bear River Basin, Colorado River Basin, and Sevier River Basin) during the 2020-2023 period. These basins have unique water use challenges such environmental (Bear River Basin/Great Salt Lake), legal (Upper Colorado River Basin), and supply (Sevier River Basin). The proposed water balance approach is run at monthly scale and addresses the change of soil water content depletion at the root zone (∆Dr) with precipitation, irrigation, evapotranspiration (without assumptions on water use efficiencies or losses), capillary rise from groundwater (CR), runoff (RO), and deep percolation (DP). CR, RO, and DP were merged as Water Balance Residual (WBR). Based on WBR and water balance components, performance metrics are evaluated from agronomical and irrigation standpoints considering effects of geography, growing season length, and agricultural water diversion along the season. These metrics will allow for a standard ICC comparison considering the different geographical conditions. Furthermore, the proposed water balance implementation and comparable metrics will help to identify opportunities for agricultural water diversion reduction. Thereby, supporting Utah’s agriculture resilience across the different climatic and landscape conditions existing in the state.

Glaciology remains deeply intertwined with Western colonial legacies, shaped by narratives of conquest, exploitation, and extraction. This project reimagines glacial landscapes by exploring their significance through Indigenous epistemologies, environmental histories, socio-political systems, and academic paradigms. Through a relational perspective, glaciers are viewed as participants of a dynamic web of relations with life, rock, water, physical forces, and anthropogenic change. This work probes our positionality and responsibilities as researchers, acknowledging the lands that sustain our livelihood and the environments that animate our work. This analysis utilizes a systematic literature review and synthesis of gray literature to confront prevalent gaps in cryospheric research and reposition glaciology within broader societal and academic contexts. Drawing from critical theory, Indigenous value systems, community-engaged methodologies, and knowledge co-production, I propose a research framework rooted in relational accountability, reciprocity, and resurgence. Intended to be adaptable, the concepts remain relevant in contexts marked by Indigenous erasure, uninhabitance, and colonial motives. By interrogating and challenging how dominant narratives of power, place, and positionality shape glaciological knowledge, this project prioritizes a transformative approach that integrates accountability, embodiment, and relationality to encourage a more resilient, inclusive, and ethically grounded practice of cryospheric research.

Permafrost impacts Arctic hydrological systems, ecosystem dynamics, and infrastructure stability, yet its fine-scale response to ongoing climate warming remains poorly constrained. Distributed acoustic sensing (DAS) offers a practical means of acquiring dense seismic data in remote regions without the logistical challenges of active sources. We deployed a 2-km fiber-optic DAS array across both disturbed and undisturbed tundra near Utqiaġvik, Alaska, and continuously recorded ambient seismic wavefields from 2021 to 2025. During winter, when anthropogenic noise is minimal, we use surface-wave energy from impulsive cryoseismic events, likely caused by thermal contraction and ice-wedge cracking, to characterize permafrost conditions. We extract several high signal-to-noise events and apply multichannel analysis of surface waves (MASW) to invert shear-wave velocity (Vs) profiles. Results reveal a high-Vs layer (2.0–2.5 km/s) at depths of 80–100 m, interpreted as massive, segregated ground ice formed under high overburden stress and low temperatures, which is consistent with our understanding of the geology of the North Slope of Alaska. Repeating the analysis throughout different time periods can reveal temporal variations in shallow Vs associated with seasonal temperature fluctuations and long-term (interannual) thermal changes. Monitoring variations provides insights into thaw-front dynamics, variability in ice content, and the stability of frozen ground over time. These findings demonstrate the potential of DAS for time-lapse imaging of permafrost with high spatial resolution, offering a powerful tool for monitoring degradation that would otherwise be logistically infeasible in polar regions.

Despite growing recognition of climate change impacts on agricultural systems, changes in water constraints on vegetation productivity across India remain poorly understood. Here we conduct a comprehensive evaluation of changes in relationship between vegetation growth and limitations in water availability across India between 1982 and 2020, analysing multiple vegetation indices (Normalized Difference Vegetation Index [NDVI], Leaf Area Index [LAI], Fraction of Photosynthetically Active Radiation [FPAR]) against multi-timescale drought indicators (Standardized Precipitation-Evapotranspiration Index [SPEI] 1-24 months). We document a substantial increase in vegetation water constraint over this period, with water-deficit areas expanding across the country during the primary cropping season of Kharif. Remarkably divergent seasonal trends emerge for the Rabi season, with less pronounced water stress patterns. The increase in water constraints was coincident with significantly decreasing response times, suggesting susceptibility of vegetation to drought events as early as within four months of water scarcity in recent years. These observed changes were found to be primarily attributable to rising atmospheric CO₂, with distinct seasonal patterns - precipitation effects dominating during Kharif while temperature effects become more prominent during Rabi. Our findings highlight the need for incorporation of water constraint in vegetation monitoring and drought assessment frameworks under a warming climate, with implications for food security across India’s diverse agricultural systems.

The Technology Innovation Thermoradiative Uranus Smallsat (TITUS) is a proposed smallsat technology demonstration concept mission to showcase an innovative power source utilizing thermal power conversion from a thermoradiative cell (TRC). The proposed TRC has 3 times higher thermal to electrical power conversion efficiency than heritage MMRTG systems with a lower volume and higher mass specific power density. This makes a TRC particularly attractive for small-scale missions to the outer planets or other locations where low-light environments render solar power impractical. The mission would primarily serve as a proof-of-concept for TRC technology through a low-risk Smallsat companion to NASA’s nominal Uranus Orbiter & Probe (UOP) Mission. The primary objective for TITUS is to demonstrate TRC powering of a functional Smallsat in communication with the UOP while conducting supporting science measurements for a duration of 5 years or more. The conceptual design for the TITUS science package would complement UOP magnetospheric science during 15 additional orbits. By separating from the UOP, TITUS would achieve distinct spatial and temporal samplings of the complex magnetic field, radiation environment, and magnetopause of Uranus. The magnetosphere of Uranus is not well explored because Voyager 2, the only spacecraft to visit Uranus, spent only hours passing through the magnetosphere. Subsequent modeling also suggests that the lone flyby may have observed Uranus during an unusual period of solar activity. The TITUS observations would fill in the three-dimensional profiles of the interaction of the magnetic field with the solar wind. Likewise the TITUS spacecraft will fill in details on the radiation belts, with multiple passes through both Uranus’ electron and proton belts. The design was developed by the NASA Glenn Compass systems engineering team in coordination with Phase II of NASA’s Innovative Advanced Concepts (NIAC) program.

Green hydrogen projects are capital-intensive. Projects employ different funding structures, through a combination of debt and equity. The cost of capital of such projects plays an important role in determining their feasibility. Yet, it is difficult to find accurate estimates of the cost of capital for green hydrogen projects because of the confidential nature of the developments. Studies tackling cost of capital for green hydrogen are few, due to the nascent nature of the technology, however existing ones rely on assumed uniform rates, stylized global scenarios, expert surveys or a priori assumptions that equate cost of capital to interest rates. Quantitative estimations based on empirical data are lacking.Knowledge of the cost of capital is important for accurate modelling of cost projections of green hydrogen and comparability with other decarbonization alternatives. It is also a vital piece of information in designing effective support policies to encourage private investment, as has formidably occurred with solar and onshore wind in OECD countries over the past decade. We carry out an econometric estimation of cost of capital for industrial-scale green hydrogen projects in Germany in final investment decision or operational phase. This study contributes to the literature by providing a geographically and technologically specific cost of capital estimate for use in energy models and aids decision makers in developing tailored policies to spur the development of decentralized green hydrogen industrial facilities.

The role of fluid in intermediate-depth seismicity has been intensely debated with proposed mechanisms ranging from those requiring the presence of significant amounts of fluid (dehydration embrittlement, hydrofracture) to those suggesting fluid’s presence being incidental (dehydration-driven stress transfer) or even unnecessary (thermal runaway). One factor preventing resolution of this debate is that the vast majority of intermediate-depth earthquakes occur within subducting slabs where it is difficult to determine the fluid distribution at length-scales relevant to the generation of earthquakes owing to large uncertainties in fluid production and migration. Recent examination of structures inferred in patterns of seismicity, and receiver functions comparing the character of the slab surface and Mohorovičić discontinuity, along with thermal modeling of Colombia’s Cauca cluster show that the cluster includes more than 1,000 intermediate depth earthquakes in the mantle wedge directly above a subducting slab experiencing intense slab seismicity and dehydration. This suggests that earthquakes in the wedge likely occur along pathways where slab-derived fluid induce hydrofracture. We use multiple seismic methods to show that the Cauca cluster mantle wedge earthquakes occur in material associated with (1) elevated Vp/Vs along with (2) high attenuation of both P- and S-waves and further that these earthquakes show (3) lower average stress-drops than slab earthquakes occurring directly below the mantle wedge earthquakes. These observations indicate that hydrofracture is likely the primary cause of mantle wedge seismicity in the Cauca cluster. We further show that characteristics of the Cauca cluster’s slab seismicity are more varied than the cluster’s mantle wedge seismicity. Our observations indicate that slab seismicity at intermediate depths is not driven by a single mechanism, even in settings like Colombia where only a single plane of Wadati-Benioff Zone seismicity is observed. Instead, the draining of fluid via hydrofracture co-occurs with at least one other seismogenic mechanism.

Recent evidence points to a relatively young age of the Earth’s inner core. Such findings challenge the classical convection-based geodynamo theory that explains the Earth’s magnetic field generation. The interaction of the liquid core with the topographic features at the core-mantle boundary, however, could modify the dynamics in a way that promotes dynamo action. Topography may facilitate increased heat transfer efficiency, propagation of inertial waves, and, as the focus of this work, modified helicity generation. Helicity, an inviscid invariant like momentum and energy, serves as a topological measure of the intertwining of vorticity and velocity in a fluid flow. It plays a central role in the classical picture of geodynamo action. Here, we aim to quantify helicity using three-dimensional flow fields measured by Lagrangian particle tracking in our newly developed apparatus, ToRoCo (Topographic Rotating Convection). We compare helicity generation in experiments with smooth top and bottom boundaries to those with rough boundaries of various length scales. Together with future measurements in liquid metal experiments, we aim to characterize how topography and helicity interact in planetary flows.

Remote sensing data have identified numerous chloride salt-bearing deposits across the Martian southern highlands. Due to the high solubility of chloride salts, these deposits may indicate the last stable liquid water on the Martian surface. Analyses of the deposits’ morphologies, ages, and surrounding topographies may provide crucial clues for understanding the hydrologic past of Mars. Many hypotheses regarding the formation of these deposits have been proposed (for example, ponding and evaporation of upwelled groundwater, surface runoff, or formation in shallow lacustrine environments), but no consensus has yet been reached, and it is possible that multiple formation pathways resulted in chloride salt deposition. In this work, we use salt map/digital terrain model (DTM) overlays to perform geomorphological analyses of chloride depositional environments. A Hapke-based abundance estimation technique was used to create salt abundance maps from Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) imagery that overlies chloride-bearing deposits. We used Context Camera (CTX), High Resolution Imaging Science Experiment (HiRISE), and High Resolution Stereo Camera (HRSC) datasets to create DTMs. We have identified 4 target regions (Terra Sirenum, N-NE Noachis Terra, W of Hesperia Planum, and E of Hesperia Planum to Terra Cimmeria) in the southern highlands and focused our analyses on ~4-5 deposit clusters within each target region, forming a representative sample of deposit variability. We have selected clusters based on diversities in how chloride deposits map with elevation, underlying deposit age (ranging from late Noachian to early/mid Amazonian), valley network density, and surrounding geologic features such as channels or flat expanses. The combination of elevation data from DTMs, high resolution imagery for surrounding context, and CRISM salt abundance maps enables us to place constraints on deposit thickness and extent, chloride salt volume, and brine-forming pond/lake volumes if applicable. Studying variability among deposits will assist in determining if chlorides formed from a universal process or multiple, localized processes. The chloride salt map/DTM overlays created in this work will enhance understanding of these deposits and assist in the determination of their origin.

FjordPhyto is a polar participatory science program committed to public engagement in scientific discovery from the world’s most remote data-limited regions. Launched in 2016, FjordPhyto partners with expedition cruise vessels for gathering environmental data in Antarctica, one of the fastest-warming regions in the world. We have successfully involved more than 10,000 travelers to date; however, our engagement with individuals outside of travel to the poles has remained limited. To expand our reach, from 2023 to present, FjordPhyto partnered with two NASA Science Activation Teams: Infiniscope (through our POLARIS and InfiniteOCEAN initiatives) and OCEANOS (through our three-year PRO OCEANOS initiative in Puerto Rico (PR)). These collaborations significantly broadened FjordPhyto’s outreach to younger learners, as well as to English- and Spanish-speaking audiences via in-person activities and online engagement platforms. We developed hands-on workshops, lecture materials and Augmented and Virtual Reality (AR/VR) assets of 3D phytoplankton and Antarctic environments for in-person engagement reaching 60 students in Puerto Rico. We harnessed Infiniscope’s digital learning platform to develop three interactive ocean theme modules, virtual field trips, and one training course to effectively train 59 tour expedition guides who facilitate FjordPhyto in Antarctica. These efforts provided invaluable opportunities for networking and enhanced proficiency in program, time, and people management, fostering critical leadership skills for Early Career Researchers as Principal Investigators. Our success comes through collaboration with experts in communication and education who provided space and funding for us to expand. Through SCoPE collaborations, we have significantly expanded our reach, engaging more individuals in polar science.

A document by Adrija Datta. Click on the document to view its contents.

Inland wetland vegetation plays a critical role in regulating hydrological and ecological processes, supporting natural flood control, improving water quality, and sustaining ecosystem functions essential to land-use resilience and biodiversity. Yet, accurate classification of their vegetation communities remains challenging due to high spectral similarity, vegetation structure, and variable sensor characteristics. This study presents the development of a foundational deep learning model designed to classify inland wetland vegetation communities in Central Florida, USA, using high-resolution WorldView-2 (WV-2) imagery. We trained a two-stage convolutional neural network (CNN) on WV-2 imagery collected over wetland management areas of the Upper St. Johns River Basin (USJRB) in Central Florida, overseen by the St. Johns River Water Management District, using labeled vegetation patches derived from field-verified expert interpretation. The first stage model distinguishes among broad structural vegetation types, which are then further classified into finer community-level categories by a set of specialized submodels. The model leverages learned spatial features and targeted spectral indices to address persistent classification challenges, including spectral similarity and shadow-induced confusion. 

Mineral dust aerosols from arid and semi-arid regions are key modulators of Earth’s radiative budget, cloud microphysics, biogeochemical cycles, and regional air quality. Ephemeral lake beds (playas) are among the most prolific global dust sources, where surface mineralogy directly influences aerosol optical properties and dust entrainment thresholds. However, mineralogical compositions in these environments remain poorly constrained in global climate models (GCMs), primarily due to the absence of spatially extensive, high-resolution observational data.To address this gap, NASA’s Earth Surface Mineral Dust Source Investigation (EMIT), launched aboard the International Space Station in July 2022, leverages imaging spectroscopy to generate global-scale maps of dust-relevant surface mineralogy at ~60 m resolution. While EMIT represents a significant advance in hyperspectral remote sensing of dust source mineralogy, its sensitivity to spatially heterogeneous and compositionally complex surfaces requires rigorous field validation.We report results from a targeted field campaign at Etosha Pan, Namibia – one of the most active Southern Hemisphere dust sources and a designated EMIT validation site. During the peak austral dust season, field spectroscopy measurements were collected using a high-resolution portable spectroradiometer across representative surface types (pre-emission, post-emission, and non-emissive substrates). Co-located sediment samples were analysed using X-ray diffraction (XRD) to determine ground truth mineralogical compositions. These paired datasets enabled direct evaluation of EMIT-derived mineral classifications, with a focus on spectrally diagnostic phases such as phyllosilicates, carbonates, and sulphates relevant to dust mobilisation.This study provides the first field-based calibration of EMIT for a Southern African playa system, establishing critical benchmarks for sensor performance in dust source characterisation. Our findings offer key insight into the sensor’s application for global dust source monitoring. These results support improved GCM representation of dust emissions and highlight the potential of orbital imaging spectroscopy for long-term, global-scale monitoring of mineral dust sources under climate-driven surface changes.

To better understand the effects of urban forests on major cities and statewide urban areas in the United States, the USDA Forest Service’s Urban Forest Inventory and Analysis (FIA) program combines field measurements with social and climatic data to provide estimates of ecosystem services. Historically, ecosystem services provided by urban trees were only calculated at the city level, but methodological advances now allow for these services (removal of carbon monoxide (CO), nitrous oxide (NO2), sulfur dioxide (SO2), ozone (O3), 2.5 µm particulate matter (PM2.5), and associated human health benefits) to be modeled for urban areas across the nation. The economic value and health impacts of the trees are based on estimates from i-Tree Eco and the Environmental Protection Agency’s Benefits Mapping and Analysis program (BenMAP-CE) which incorporates population, baseline healthcare costs, and local air pollution measurements. Here we analyze how modeling ecosystem services for individual trees improves our understanding of synergies between air pollution and urban systems. Based on urban FIA data from the cities of Madison and Milwaukee, WI and the urban areas across Wisconsin, we found that, on average, the statewide urban areas had higher canopy cover (22%) than the two cities, and that the urban forests of WI remove15,000 tons of air pollution annually. However, on a per tree-cover basis, the trees in Milwaukee had 4 times the air pollution removal value of Madison ($930 per acre tree cover vs $233 per acre tree cover), and 2 times the value of air pollution removal for urban areas in general ($449 per acre tree cover). These results highlight the presence of regional variation in air pollution removal by trees across urban systems and show that by shifting from a city-level ecosystem service assessment to individual tree estimates, we are able to obtain more localized and regionally representative estimates of ecosystem services provided by urban forests.

High-resolution irrigation mapping is critical for understanding agricultural water use, supporting food security, and informing sustainable land and water management policies. However, existing global irrigation maps are often too coarse, infrequently updated, and reliant on labor-intensive ground-truth data collection. While machine learning models have shown promise in classifying irrigated areas, their effectiveness is often limited by the scarcity of high-quality labeled data, especially in small-scale regions that are not typically captured by existing surveys or global datasets. We introduce an interactive labeling interface that integrates active learning with human feedback to accelerate the creation of high-quality irrigation datasets. Leveraging openly available environmental datasets from agencies such as the United States Geological Survey (USGS) and NASA, the interface enables scientists to define initial labeling criteria such as threshold values for variables known to correlate with irrigation (e.g., vegetation indices or land surface temperature) to generate a preliminary set of labeled data. After training an initial model on this data, the interface then presents scientists with a small subset of uncertain or high-impact predictions, which can be reviewed and relabeled using domain expertise. The updated labels are incorporated into the training dataset to iteratively retrain the machine learning model. This targeted feedback loop reduces manual annotation effort and improves model accuracy and generalization across diverse regions. Our approach provides a scalable and reusable workflow for generating high-fidelity irrigation maps at high spatial resolution, while also establishing an interface that enables scientists to directly guide model development, particularly in regions with sparse data coverage or limited ground-truth information.

The moon Io, the most volcanically active body in the Solar System, functions as the “energy converter” of the Jovian environment: tidal heating by Jupiter and its Galilean satellites powers intense outgassing that sustains both the neutral and plasma tori and feeds the Jovian magnetodisk via centrifugal processes. Spacecraft traversals and remote observations, from ground‐based telescopes to JAXA’s Hisaki, have revealed the Io Plasma Torus’s (IPT) intricate spatial architecture and variability across timescales from hours to decades. Yet, fully characterizing the drivers of IPT structure and magnetospheric dynamics remains an open challenge. In this respect, NASA’s Juno mission, in its highly inclined, low‑perijove trajectory, provides in situ measurements of magnetic perturbations, plasma composition and abundance, through both plasma wave and radio occultation experiments, while refining tidal‐dissipation models through gravity science. Complementarily, a future network of longitudinally distributed Earth‑based observatories will deliver continuous, global monitoring of auroral emissions and torus brightness, capturing rapid fluctuations that single‐station views cannot. The synergy of Juno’s detailed local sampling and round‑the‑clock remote surveillance promises a holistic view of source–sink interactions driving Jupiter’s magnetosphere and the Io torus.

Low-frequency (LF) seismic data are crucial for stable full-waveform inversion (FWI), yet remain difficult to acquire due to source bandwidth limitations and noise. We propose a novel physics-aware conditional diffusion framework to extrapolate LF wavefields from observed high-frequency (HF) data. Our method models LF extrapolation as a conditional generation task, using a neural network trained via denoising diffusion to approximate the gradient of the log-probability of LF data given the HF observations, also known as the conditional score function. During sampling, we enforce physical consistency by introducing a soft constraint based on the plane-wave partial differential equation, leveraging the observation that HF and LF wavefields share similar local slopes. This guides the generative process toward kinematically plausible extrapolations. We demonstrate on synthetic datasets that our method improves amplitude coherence and continuity in complex waveform settings. Figure 1 shows a scheme with an overview of the proposed methodology. The approach also supports uncertainty quantification through posterior sampling: each diffusion trajectory yields a different plausible LF reconstruction. This capability is particularly valuable in FWI, where LF data strongly influence the recovery of long-wavelength velocity models and proper initialization helps mitigate local minima. By analyzing variability across LF realizations, we gain insights into inversion uncertainty. Overall, the method combines data-driven generation with physics-based guidance to enable realistic, interpretable LF reconstructions with direct relevance for seismic inversion workflows.

Permafrost degradation linked to rapidly rising air temperatures across the Arctic drives geologic hazards, such as weakened ground bearing capacity and differential ground subsidence. This threatens to disrupt the lives of millions of people living in the Arctic by damaging infrastructure needed to sustain over 1000 high-latitude communities on permafrost. Publicly-available geospatial data, such as OpenStreetMap, used to estimate exposure of infrastructure to hazardous permafrost degradation is limited in geographic coverage across the pan-Arctic. This has led to inaccuracies in the estimated spatial distribution of risk. Using a deep learning model trained to detect buildings from sub-meter resolution Maxar satellite imagery of Arctic communities, we mapped a building area of 52 million m2 (454,947 individual buildings) missing from the OpenStreetMap dataset, expanding the mapped area of buildings by 22% at the pan-Arctic scale. Building area was expanded by 45% in Alaska, 23% in Russia, and 13% in Canada. The highest total area of new buildings was mapped in Russia at 42 million m2 (384,850 buildings). Combining OpenStreetMap with this newly mapped building area suggests that 64% and 52% more pan-Arctic building area is exposed to highly-hazardous permafrost degradation by 2041-2060 under the RCP4.5 and RCP8.5 scenarios, respectively.