Aquaculture is one of the fastest growing sectors in global seafood production. Previous studies have identified oysters' vulnerability to increasing atmospheric CO2 and ocean acidification, while few studies have investigated the feedback of oyster aquaculture on carbon emission and carbonate chemistry. In Narragansett Bay, Rhode Island, USA, the local aquaculture industry has observed a worrisome trend: high oyster mortality in recent years. Through a collaborative effort with local oyster farmers in Narragansett Bay, we aim to better understand the drivers of this aquaculture trend. We initiated a high-temporal resolution time series of baseline environmental data to measure the natural variability of temperature, salinity, dissolved oxygen, chlorophyll, and pH near 10 oyster farms in lower Narragansett Bay. Additionally, we deployed juvenile and adult oysters in traditional surface and bottom gear and newly approved alternative surface gear to monitor growth and respiration. In the first observation season, summer 2024, growth measured as shell height in both types of surface deployment was .0986 mm day-1, more than double the rate of the oysters measured from bottom deployment. The preliminary results also showed significant biogenic calcification in summer months, determined from the Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC) anomalies compared to conservative mixing curves. The average TA anomaly due to calcification was -39 µmol kg-1 when temperature was >19 °C, while net dissolution, or positive TA anomaly (+28 µmol kg-1), was observed in cold season (<14°C), a sign of corrosive and stressful conditions for bivalves in Narragansett Bay. Analyses estimated the annual cycle of carbonate chemistry changes, including air-sea CO2flux near the oyster aquaculture sites, while the annual archived oyster landing data were used to cross-check the mass balance estimation. Through examining the impact of environmental factors on oyster growth rates, and oysters’ feedback to the surrounding carbonate system, this study benefits our understanding of the limitation and sustainability of the expanding aquaculture in coastal regions.

Microearthquakes (M<2.5) recorded by borehole optical fibers with Distributed Acoustic Sensing (DAS) retain abundant high-frequency energy, allowing us to characterize the source of small events where surface stations often lack enough bandwidth to analyze. The spectral ratio method is advantageous for DAS because the method isolates the source term and removes other unwanted effects. For other approaches, we can use the densely spatially distributed DAS receiver channels to estimate the attenuation structure, which is necessary to obtain reliable source parameter measurements.

Tomorrow.io operates a near-global, near-real time precipitation retrieval framework that extends coverage beyond traditional radar networks. This manuscript details the development and operationalization of the product's third generation, which is currently available with a refresh rate of 5 minutes and spatial resolution of 0.04° (~4 km), covering latitudes between 60°S and 72°N. Precipitation estimates are derived from a convolutional neural network that ingests observation sequences from multi-channel geostationary imagers and orbiting microwave sounders within the preceding hour. The network is trained to handle sparse data from individual instruments, delivering a spatially-complete retrieval that approximates real-time conditions. Our results demonstrate that this third generation of the Tomorrow.io near-global precipitation product improves categorical and continuous precipitation metrics by 5-10% over the previous generation, with significantly larger skill improvements over publicly available near real-time alternatives such as IMERG-Early. Verification was performed against terrestrial radar networks (USA, Europe, Japan, Brazil, and Australia) using test datasets spanning all seasons and against gauge observation networks for several case studies outside the training domains. Additionally, case studies of retrievals in impactful hurricanes during 2024 and recent operational results demonstrate the impact of incorporating microwave sounder data from two recently launched instruments in Tomorrow.io's constellation. These instruments provide more direct observations of precipitation processes than the geostationary imagers, which the network leverages to further enhance skill beyond the third-generation product's elevated baseline. The Tomorrow.io satellite constellation and precipitation retrieval framework benefit near-real time weather applications in aviation, agriculture, hazard preparedness, water resource management, and other sectors at global scales.

Collection, management, and sharing of environmental sensor data require hardware and software to support the day-to-day data management needs of scientists and practitioners who operate networks of environmental sensors and dataloggers. Given the volume of data produced, it is challenging to consistently produce data products of sufficient quality for use in operational or scientific contexts. Specific challenges include data retrieval from field sites, provisioning performant storage, specification of unambiguous metadata, mediation across the different formats, standards (or lack of standards), protocols, and vocabularies used by various sensor and datalogger manufacturers, data quality control and versioning, and integration with reputable data repositories for sharing and publication. Past efforts, including the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), provided software tools that met many of these needs but are now more than a decade old, leaving few reliable options for sensor data management. While viable commercial software options exist, the cost of some systems is beyond the reach of many data collection organizations. Other solutions are tied to specific datalogger/sensor manufacturers with no interoperability, making it difficult to collect and manage data where a diversity of sensors is required. Additionally, new ways of collecting data using Internet of Things (IoT) devices have recently emerged along with new standards for collecting, describing, and sharing sensor data, including the Open Geospatial Consortium’s (OGC) SensorThings standard. These new methods and standards help with interoperability but lack sufficient software implementation for easy adoption. In this presentation, we describe the open source HydroServer software, which was developed to enable collection, storage, management, and sharing of data from environmental sensors deployed at in situ monitoring sites. HydroServer provides web-based management of in situ sensor data, integration of OGC’s SensorThings application programming interface and data model for automating data ingestion and data querying, deployment in the commercial cloud, and integration with the HydroShare repository for data sharing/archival.

The diverse community of Critical Zone (CZ) scientists study the coupled chemical, biological, physical, and geological processes operating across scales to support life at the Earth's surface. Data produced by CZ scientists are as diverse as their creators, ranging from time series of observations from in situ sensors to laboratory analysis of various types of physical samples, geophysical measurements, microbiological sampling, and many other data types. In 2020, the U.S. National Science Foundation funded a network of CZ Thematic Cluster projects called “CZNet” to work collaboratively in answering scientific questions related to effects of urbanization on CZ processes; CZ function in semi-arid landscapes and the role of dust in sustaining these ecosystems; deep bedrock processes and their relationship to CZ evolution; CZ recovery from disturbances such as fire and flooding; and changes in the coastal CZ related to rising sea level. The CZ Coordinating Hub’s goal is to make data, samples, software, and other research products created by CZ Net projects Findable, Accessible, Interoperable, and Reusable (FAIR), using existing domain-specific repositories. This presentation will focus on several of the challenges the CZNet Hub project has encountered in working toward this goal. We will discuss our experiences in coordinating data collection, archival, discovery, and access for CZNet through development of shared cyberinfrastructure and best practices for data sharing across the network of Thematic Cluster projects. Challenges include the diversity of CZ research, which meant that no single domain data repository was sufficient. To address this, we built a data submission portal for multiple repositories and a catalog to enable discovery of research products wherever they are deposited. Helping investigators choose the “best” repository remains a challenge as this is not only related to the scientific domain of the data but also the perceived level of effort required for sharing data. Data management techniques that enable reproducibility of results and further synthesis of data are also challenges for which solutions involve development of shared best practices such as including code and scripts for data preparation and analysis activities, training, and adoption by the community of scientist

A document by Melanie J Frost. Click on the document to view its contents.

The US higher education system is a workforce development, research, and innovation powerhouse, with deep ties to local and regional communities and a history of responding to global emergencies. Supported by increasing federal recognition of the need to move beyond disciplinary and institutional silos, the higher education system is starting to energize interdisciplinary efforts and turn its collective focus to the full complexity of the climate crisis. All parts of the sector – from two-year colleges to research universities – are essential to climate action.For HEIs to address the climate crisis holistically and as a central institutional priority requires changes in both institutional and public policies, but many HEIs are willing and enthusiastic, and many of the hardest pieces are already in place. We suggest that resources, policies and federal coordination efforts focus on strengthening HEIs in three core areas of impact.First, HEIs have an infrastructure to educate millions in everything from the skilled infrastructure labor essential for green energy, to AI for efficient solutions, togenerating climate impact projections and translating climate information into usable knowledge. Second, HEIs also have a research and innovation infrastructure that drives discoveries and understanding, and HEIs are increasingly embracing the critical steps of understanding the social impacts and acceptability of new innovations and of getting those innovations to market. Third, many HEIs, already working in service to the public good, have deep long-standing relationships with the most vulnerable communities in our country and are starting to provide climate services and serve as a trusted source of climate information.With relatively small investments and national coordination and leadership, the higher-education network could help to propel the US back into a global leadership position on climate action and to build a climate-ready workforce and a thriving green economy.

Distributed acoustic sensing (DAS) on submarine fiber-optic cables is providing new observational insights into solid Earth processes and ocean dynamics. However, the availability of offshore dark fibers for long-term deployment remains limited. Simultaneous telecommunication and DAS operating at different wavelengths in the same fiber, termed optical multiplexing, offers one solution. In May 2024, we collected a four-day DAS dataset utilizing an L-band DAS interrogator and multiplexing on the submarine cables of the Ocean Observatory Initiative's Regional Cabled Array offshore central Oregon. Our findings show that multiplexed DAS has no impact on communications and is unaffected by network traffic. Moreover, the quality of DAS data collected via multiplexing matches that of data obtained from dark fiber. With a machine-learning event detection workflow, we detect 31 T waves and the S wave of one regional earthquake, demonstrating the feasibility of continuous earthquake monitoring using the multiplexed offshore DAS. We also examine ocean waves and ocean-generated seismic noise. We note high-frequency seismic noise modulated by low-frequency ocean swell and hypothesize about its origins. The complete dataset is freely available.

A document by Jiaxi Zhao. Click on the document to view its contents.

A document by Sarah C Kavassalis. Click on the document to view its contents.

A document by DARSHAN MEHTA. Click on the document to view its contents.

The intensity of Galactic cosmic rays (GCRs) is affected by solar wind structures such as coronal mass ejections (CMEs), their interplanetary counterparts (ICMEs), and stream interaction regions (SIRs), giving rise to temporal decreases in the cosmic ray intensity, which can be recorded with neutron monitor data, these variations observed at the Earth surface can be a valuable tool to analyze and predict space weather phenomena. However, some of these decrements cannot be linked to significant structures in the local interplanetary medium. In other words, we may have events not detected at L1 close enough to Earth to affect neutrons. They might be significant even though Wind or ACE does not detect them. In this work, we look for this type of decrements during the beginnings of solar cycle 24 using heliospheric imagers onboard STEREO, allowing us to observe the region between the Sun and Earth in a manner that was never followed by imaging cameras continuously, allowing us to determine enhancements in density observed by heliospheric imagers may explain previously unexplained cosmic ray decreases.

Accurate estimates of snow water equivalent (SWE) are essential for understanding hydrological processes and managing effective management of water resources, particularly in snow-dominated regions. Many methods for estimating SWE rely on in-situ measurements and/or numerical models. In-situ measurements, such as those provided by the USDA Snotel network, have the advantage of being direct observations of SWE but are only sparsely available and suffer from challenges of representativity. At the same time, numerical models embed knowledge of the physical processes underlying the snowpack accumulation and ablation but can be computationally expensive to run over large areas. In this study, we investigate applying deep learning techniques to predict the spatiotemporal distribution of SWE from a combination of atmospheric forcings derived from the Weather Research and Forecasting (WRF) model, geographic parameters related to topography and land cover that influence snow persistence, and historical observations of snow presence/absence from remote sensing data. By leveraging static variables and dynamic atmospheric forcings from WRF  as input features, we train a convolutional long short-term memory (ConvLSTM) network to predict SWE. Our proposed deep learning model aims to accelerate the prediction of spatially distributed SWE compared to traditional methods and can complement process-based land surface models often used to predict SWE. The computational savings associated with training and forward integration of machine learning based models open the door to high-resolution ensemble forecasting of SWE and assimilation of observations for real-time SWE estimation.

A document by Chunzeng Wang. Click on the document to view its contents.

A document by Mary Keller. Click on the document to view its contents.

A document by Mary Keller. Click on the document to view its contents.

A document by Abdelhamid Ads. Click on the document to view its contents.

AGU 2024-iPosterSessions-an aMuze! Interactive system https://agu24.ipostersessions.com/Default.aspx?s=E0-2B-67-5C-28- ...