FY 2021 Small Business Innovation Research Phase II

A compelling case can be made for increasing scientific and technical knowledge for aquaculture to produce safe and nutritious seafood in the United States, create new jobs from the coastal communities to the agricultural heartland, foster sustainable aquaculture practices, and enhance or restore wild fisheries and habitats. The U.S. has bountiful freshwater and marine natural resources, plentiful feed grains, adequate aquaculture research infrastructures, and excellent scientists, pioneers, and entrepreneurs to drive innovation. The seafood supply chain ranges from farmers and fishermen to upstream and downstream industries (feed and equipment manufacturing, harvesting processing, distribution, and retail outlets) to consumers. A dramatic increase in domestic aquaculture is needed to complement well-managed wild-harvest fisheries and help meet the growing demand for seafood, a food source high in healthful protein and omega-3 fatty acids with many essential vitamins and minerals. Currently cost effective tools to track seafood safety from harvest to plate are not readily available to farmers. By investing in low cost farm management tools, farmers would be able to bring their product to market in many ways - from direct farm sales to commercial sales. Projects could range from app development to allow for inventory system of shellfish on a farm (sizes, locations, harvest availability); real time tracking of toxic blooms and real time analysis of project on farms that is cost efficient to allow for direct sales; cost effective tools to track temperature of aquaculture projects from farm to table; etc.

Risks to aquatic animal health within the context of marine aquaculture are a critical concern for both the economic security of seafood producers in the US and for the health and safety of seafood consumers. The ability to ensure and demonstrate a culture environment free from pathogens reduces those risks, and provides a potential marketing advantage for farmed products (thereby reducing pressure on wild fisheries). Tools to detect disease in these settings are limited, in both availability and relevance, and there have been no meaningful advancements in practical biosecurity measures for aquaculture in recent years. Products produced as a result of this subtopic should be practical (i.e. not cost prohibitive for aquaculture practitioners and not prohibitively complicated to deploy) and effective. Tools for disease detection should focus on pathogens of concern (those known to pose an economic threat to growers and those that pose a known threat to human health) and should deliver actionable results to the end user (i.e. indicate whether the presence of a particular organism indicates a true risk).

Worldwide (but especially in the United States) aquaculture lags far behind other methods of protein production with regard to genetic tools used to increase production efficiency, protect the health of farmed and cultured organisms, and to protect the wild populations in adjacent habitats. While there have been a number of relatively recent advancements related to brood stock selection for both finfish and bivalves, there are numerous other areas where genetic tools have not yet been developed and deployed. Innovative tools produced as a result of this subtopic should focus on reducing risk carried by marine aquaculture operations by addressing one of the three focus areas indicated: 1) Improving the health of finfish, shellfish or seaweed being raised on marine farms; 2) Increasing the productivity of marine aquaculture operations, such that more product and/or value can be created with an equal or lesser impact on the environment; 3) Reducing the potential for negative impacts on plants and animals that may exist in proximity to marine aquaculture installations.

Substantial progress has been made in mapping the distributions of metal micronutrients throughout the ocean over the last 30 years, but there remain information gaps, particularly during seasonal transitions and in remote regions. Trace metal micronutrients are integral to the functioning of marine ecosystems and the export of particulate carbon to the deep ocean. A remaining challenge is to develop in situ sensing technologies necessary to capture the spatial and temporal variabilities of micronutrients characterized with short residence times, variable sources, and nanomolar to sub-nanomolar concentrations in open ocean settings. Development of these sensors will allow investigation of the biogeochemical processes at the necessary resolution to constrain fluxes, residence times, and the biological and chemical responses to varying metal inputs in a changing ocean. To encourage a more widespread use of in situ sensors by academics and resource managers, the challenge is not only to develop devices that are robust, compact, easy to operate and amenable to long term deployments (>month) but also to produce sensors that can meet or come close to the same stringent accuracy and precision criteria that are achieved in the laboratory. Specifically, one unmet need is a sensor capable of determining Fe concentrations in real time on CTD rosettes, moorings, and autonomous vehicles. Significant progress can be made towards the development of an ocean Fe-sensor based the new generations of microfluidic, solid state, voltammetric, and other technologies. For example, advances in “Lab-on-a-Chip” technology combined with new chemical probes show promise in achieving limits of detection required to understand the oceanic Fe system. Some of these probes (such as for Zn) have been successfully adapted to seawater analysis at sub-nanomolar levels in the laboratory but none have been adapted for Fe, nor for in situ use. Similarly, immobilization of fluorescent probes onto fiber optic style sensors may hold promise as well. Electrochemical techniques exhibit high sensitivity in the lab, and their use in in situ sensor-systems is an area of possible development. The overall goal is the production of an in situ sensor capable of analyzing Fe at low to subnanomolar levels for periods of time exceeding 1 month. The chosen technology would preferably be applicable to other trace nutrients.

A low-cost temperature and depth sensor package that utilizes wireless data transmission (satellite, cellular, WIFI) does not currently exist in the market, and would have a broad interest by scientists and the fishing industry deploying instrumentation on commercial fishing gear. Needed features for the temperature depth sensor package include: high accuracy; fast satellite data acquisition and transmission; extended battery life; waterproofing; and ruggedized design. Developing a low-cost system for oceanographic monitoring on commercial fishing gear would open opportunities for cooperative fisheries research and ocean modeling. There is a need to design, test, and make commercially available a low-cost temperature and depth system that can be deployed on commercial fishing gear, wirelessly communicates with onboard computers, and transmits data via satellite, cellular, or WIFI. A secondary goal is to visualize the temperature and depth data collected by the sensors in near real time.

Detecting and identifying marine mammals and other protected marine species (such as seabirds, sharks, and sea turtles) and collecting biological information is a singular concern for the scientific community, as well as the oil& gas, renewable energy, and fishing industries, including aquaculture. Specifically, these different sectors need information on the number, location, type of marine species, and distance to activity, vessel, or observation platform to either cease activity, collect additional data, or continue ongoing operations to satisfy monitoring requirements. Similarly, from a permitting perspective and to comply with statutory obligations, these different groups need abundance and density estimates to determine immediate and long-term impacts on marine mammals or other marine species that may be susceptible to population-level impacts in the area of operations. Understanding the abundance, distribution, and density of animals is critical for industry to evaluate where to position their activity in the short- and long-term. Traditional methods for estimating abundance, density, and distribution for multiple cetacean species rely almost exclusively on visual surveys conducted onboard ships, rigid verify species identification in situations where the animal is too far to identify or close-in approaches to verify species identification is not possible. The automation of the readings from the long-range binoculars has enormous benefits for monitoring and mitigation for both the research and commercial industry. Fisheries, oil& gas, renewable energy, defense, aquaculture sectors, and scientific research groups rely on human observer data to input into mathematical and statistical models to obtain reliable abundance and density estimate for various protected marine species. By improving the ease of data collection and accuracy of readings, these different sectors can improve the quality of data collected and require fewer trained observers in the long-term, thereby reducing costs. There is a need to design, test, and make commercially available Big-Eye binoculars that can digitally show reticle measurements and bearings as the binocular is swiveled by the observer and simultaneously be recorded in a computer database. A secondary goal is the ability to obtain images or video of the observer visual field during a sighting or tracking of the animal. ocean platforms, or from shore. Such surveys involve the use of “Big-Eye” 25 x 150 binoculars to manually scan for different marine species, especially marine mammals to a maximum distance of about 11-13 km from the ship. Scanning is done by trained observers who locate and identify species and estimate group sizes, which are ultimately used to estimate population abundance and in the development of habitat models. Two key measures are obtained using the binoculars, which include bearing and reticle distance to the sighted animal. While the bearing measurement is easy to obtain with accuracy, the reticle distance, however, is at best an estimate due to the motion of the vessel and sea state. In addition, reticle distance measurement errors can be compounded at distance and when the target animal is being tracked. Like theodolite readings obtained on land, automated reading of bearing and reticle distance measurements would reduce or eliminate uncertainty while recording animal sightings. Further, a second issue is the lack of any photographic or video evidence of what the observer sees through the binocular. The availability of an image or video would help to verify species identification in situations where the animal is too far to identify or close-in approaches to verify species identification is not possible. The automation of the readings from the long-range binoculars has enormous benefits for monitoring and mitigation for both the research and commercial industry. Fisheries, oil& gas, renewable energy, defense, aquaculture sectors, and scientific research groups rely on human observer data to input into mathematical and statistical models to obtain reliable abundance and density estimate for various protected marine species. By improving the ease of data collection and accuracy of readings, these different sectors can improve the quality of data collected and require fewer trained observers in the long-term, thereby reducing costs. There is a need to design, test, and make commercially available Big-Eye binoculars that can digitally show reticle measurements and bearings as the binocular is swiveled by the observer and simultaneously be recorded in a computer database. A secondary goal is the ability to obtain images or video of the observer visual field during a sighting or tracking of the animal.

Coral reef ecosystems are suffering globally from the effects of ocean warming and other stressors. Dealing with these issues is paramount to the long-term existence of reefs. However, active propagation of corals is critical to maintain reefs in the interim. Coral restoration has been shown to be successful at the reef scale, but significant scale- up and improved efficiency is necessary to be successful at larger scales. While coral restoration can take on different forms from the deployment of fully grown corals (greater then 20cm and weighing in excess of 1 kg) to placement of small coral fragments (1cm and nearly weightless), almost all forms involve permanently securing small or large corals to existing or artificial reef. Today corals are primarily secured to the reefusing a hand mixed two-part adhesive that has a clay/putty like consistency. This is time consuming, as the reef site has to be prepared; the adhesive needs to be hand mixed; set-up time is slow; and the material is not “tacky” and therefore requires precise placement. Currently, the time involved in physically attaching corals is one of the major bottlenecks to efficient coral restoration. Applications in this subtopic might include innovative ideas for adhesives that are simply used underwater. While not required, some areas of interest within this subtopic include ones that address the following: • Negatively buoyant in seawater. A consistency that is tacky/sticky under water immediately with no wait time - that “grabs” both the substrate and the coral. • Able to be deployed from a “caulk gun” type device as well as from a small nozzle or syringe. • Ability to be used in large/bulk amounts (20ml for entire corals) or small/precise amounts (1ml for microfragments) depending on use and deployment device. • Requires minimal surface prep. • Initial set-up (requires external force to break free) time of 1- 2 minutes but less than 10 min. • Full cure setup time of less than 12 hours, • Able to work in salt water. • Non-toxic to marine life. • Minimal to no surface preparation.

On November 5, 2015 H.R. 774 was signed into law as the “Illegal, Unreported, and Unregulated Fishing Enforcement Act.” This legislation advances U.S. efforts to prevent illegally harvested fish from entering our ports and market and achieve sustainable fisheries globally. It also helps address key priorities in the action plan for combatting illegal, unreported, and unregulated (IUU) fishing and seafood fraud. Per the Presidential Task Force on Combating IUU Fishing and Seafood Fraud, the full extent of seafood fraud is difficult to determine, particularly as it often happens at the retail level. Cooperation with state and local authorities on addressing seafood fraud is essential in strengthening links of the supply chain that occur intrastate, or at the local level, and are sometimes outside federal jurisdiction. There is a need for technology development to design, test, and make commercially available methods to rapidly detect IUU in the market place. Applications in this subtopic might include innovative technologies that develop rapid methods or technologies to check on more of the U.S. seafood supply than is currently available. These include, but are not limited to: • Methods/Technologies to preventing aqua-cultured imports with banned pharmaceuticals from entering the U.S. • Development of rapid species identification methods in restaurants, markets, etc. • Methods/Technologies to efficiently monitor the U.S. market supply for product quality and safety. • Methods/Technologies to conduct rapid and cost effective surveys to see if restaurants are substituting high-quality seafood for cheaper imported seafood. • Methods/Technologies that support monitoring efforts of imports to prevent IUU fish from entering the U.S. market, allowing consumers to have confidence that the seafood they purchase was harvested legally and responsibly.

Space weather represents a significant risk to the U.S. electric power grid through its susceptibility to geomagnetic activity and the resulting induced electric fields. There is a need for improved understanding of the induced electric field impact on the bulk power system. NOAA has developed an operational geoelectric field nowcasting capability with longer-term plans to support short-term forecasting as well. However, this environmental specification must be coupled with analysis tools to support both static and real-time assessment of the impact of these induced electric fields on bulk power system performance and stability. This SBIR application will seek to stimulate growth in this area to ensure environmental specifications can be best applied in mitigating the space weather risk to this critical infrastructure sector. The goal of this project is to promote the development of private-sector value-added products that address the impacts of space weather on the electric power industry. This activity will: 1) survey the bulk power industry to understand the best application of induced electric field nowcasts to support both historical and real-time system analysis 2) develop value-added test products for evaluation, utilizing currently available data and model output; 3) provide recommendations on product improvements that would facilitate the development of value-added products for the private sector to address electric power industry needs. Eventually this capability could easily be commercialized for use by local and regional power companies that could be vulnerable to the effects of the dangerous electric fields produced by the sun. It could evolve into a tool or a combination of tools that not only benefits power companies but also large companies (running computer servers, transportation systems, hospitals and HVAC) that depend on consistent power grids.

The weather enterprise produces a large number of products and services the value of which cannot be fully realized without increased public awareness and engagement. This SBIR subtopic seeks the development of novel outreach tools and technologies to increase public awareness of the broad mission areas of weather support in a weather ready nation. It also seeks to stimulate engagement by quantifying and communicating the benefits of the many weather products and services, and helping to transition them to the other sectors of the economy. Activities under this SBIR subtopic might include: • demonstrating methods communicate economic benefits of weather products and services across various sectors and over time, • expanding social science efforts to better communicate the importance of weather forecasting and climate prediction services, • developing outreach tools and technologies to educate and engage the public to increase awareness, understanding, and value of the oceans, and • improving the utilization of the government-provided real-time space weather and model output data to encourage private-sector development of value-added products and services that address specific needs of the electric power industry.

There is a major need for accurate and timely observations and predictions of the altitude in the atmosphere of the rain-snow transition boundary (“snow level”) in the mountainous regions of the United States spanning the time period from September to May. Winter storms frequently impact major interstates and highways, including Interstate 80 through the Sierra Nevada and Tetons, Interstates 70 and I-79 through the Appalachians, and Interstate 90 through the Cascades and Bitterroots. As the snow levels change during a winter storm, so do the number of miles of highway that are impacted by snow and more miles of highway that need to be plowed and/or closed. Adverse weather in various mountainous and remote regions is one of the major causes for delay on the roadway system which can add significant costs to shipping resulting in overall negative impacts to the economy. In regards to public safety, observation systems that monitor rain-snow level evolution in the atmosphere are very sparse throughout the western and eastern mountain ranges. High spatial and temporal resolution observations and predictions with sufficient temporal and geographic coverage to match the scale of winter storms and traffic flows will help with travel planning with the public and commerce. These high resolution observations and predictions would need to be readily available with low latency and adequate accuracy to NOAA and the public and using relatively low-cost instrumentation to permit deployment of a wide network of sensors in hazard-prone regions. These sensors that would provide the observations and follow-on predictions could provide improved weather conditions data oriented towards forecasters, public, and freight industry and could lead to development of a hazard avoidance system for the public. The sensor data could also provide a critical dataset for academia to help improve snow level forecasting, which is a major challenge for forecasters. This would ultimately benefit decision makers in private industry, public safety, and freight movement planning. Improved snow level forecasts could help mitigate costs of shipping by allowing implementation of shipping models to account for adverse weather by shipping products earlier, later, or stopping in route. Having an improved handle on snow levels would avoid loss of revenue due to unexpected closures and hazardous driving conditions for trucks. Snow level forecasts also directly affect flood forecasts and warnings and associated rivers and streams would provide an additional benefit to the public, industry, and emergency management community when planning for large scale flood events. This information would benefit planning for agriculture and avoid problems with survival of farm animals during spring months.

With the technology advances in UAS systems, which includes sensors and platforms, it is of great interest to federal, state, and local governments to fully exploit the unique capabilities of UAS, which are expanding rapidly, to meet mission requirements for responding to natural disasters. These organizations will benefit from the commercialization of research and technology development in UAS capabilities to help enhance response to natural disasters. Projects for this subtopic should include the design and execution of CONOPS utilizing UAS observing systems to enhance emergency response, crisis management and interoperable communications between organizations and agencies. This could include rapid deployment of UAS to provide critical information to the Incident Command, First Responders and PSAP (Public Safety Answering Points / 911). Products will provide appropriate response personnel situational awareness and actionable information useful during the response, damage assessment, and/or recovery phases of disaster response efforts. Fusion of information into existing communication and developing technology hardware is critical to saving lives, assessing situations and quick response disaster relief. Utilization of UAS systems will augment government organizations response capabilities and have a substantial impact benefiting society, the ecosystem and the environment. Proposal submissions should include a clear set of plans and protocols for intra- and/or inter- agency communications, indicating how these technologies and interactions would be executed both in advance of disaster and hazmat events as well as during actual disaster and hazmat response efforts. An area of interest is to improve UAS real-time/near real-time data and product dissemination and communications such that it can have an impact pertaining to the evolution of disasters or events. This could include but not limited to ingress / egress routes, locating and providing aid to those in need, etc. Concepts of operations for response must provide societal benefit.

With the technology advances in UAS systems, which includes sensors and platforms, it is of great interest to organizations that collect meteorological data to fully exploit the unique capabilities of UAS, which are expanding rapidly, to meet data requirements for improved weather forecasts. These organizations will benefit from the commercialization of research and technology development to enable beyond line of sight UAS flight to help enhance atmospheric measurements. Continuous measurements of temperature, relative humidity, and turbulent winds in the lower layer of the Earth’s atmosphere from the surface up to 3km (9,843 feet) above ground level (AGL) have great potential to improve hazardous and extreme weather forecasts. UAS with calibrated meteorological sensors now have the ability to make these measurements and have been approved by other non-U.S. civil aviation authorities. However, U.S. companies have not yet been successful in developing a UAS approved by the FAA for beyond visual line of sight (BVLOS) for meteorological profiles. In the U.S. BVLOS flight operation requires mitigations to meet FAA sense and avoid requirements (FAR 91.113). Federal agencies and industry are beginning to be successful in receiving approval for UAS operations beyond visual range using a variety of mitigation strategies. It is a priority and imperative to accelerate BVLOS technology through innovative research and mitigation strategies such as air/ground based radar, aircraft detection technologies, and through safety risk analysis and airspace density studies. Development of BVLOS technology is a high priority need in the UAS industry. Successful BVLOS technologies will continue to be in demand as UAS utilization increases. A successful project for this subtopic would provide the technological, engineering, and/or data driven solution that mitigates the risk of collision of the UAS with other aircraft for vertical flight profiles from the surface to 10,000 ft above the ground. Typical flight times are expected to be no more than 45 minutes and be contained within a 1,500 ft radius. The technical solution should be capable of being integrated with both Vertical Take-Off and Landing (VTOL) and fixed wing UAS that are currently being operated by industry and scientific community. The new prototype UAS will need to have the capacity for integrating atmospheric sensors or use a UAS that already has the atmospheric sensor. This subtopic seeks a technological solution that can be proposed to the FAA to enable BVLOS flight for atmospheric profile measurements. Locations for initial test and development are not critical. The long term goal is for meteorological organizations to have the ability to deploy this new BVLOS system for atmospheric vertical profiles in locations that have the most impact for improving forecasts of extreme weather. Note: UAS BVLOS atmospheric profile measurement flights are currently being conducted in other countries where airspace can be segregated by civil aviation authorities for UAS flights. This is not an acceptable means of obtaining BVLOS flight in the United States.

The objective is to increase the spatial and temporal resolution of observations of pressure, humidity, temperature and wind direction/speed via an innovative framework of new sensors outside of normal radiosondes that pulls rawinsonde data from jets. This should expand upon the CONUS radiosonde network with data being available at multiple locations, more than twice per day and over altitudes so as to improve and vastly increase data that gets assimilated into forecast models. Project will address need for increased observations above the PBL even across oceanic regions and remote areas. The goal is to ensure reusable sensors, use of various means of communication for relaying data across vast distances (including over oceans) to receiving stations, integration of tracking methods for use in analysis schemes and weather prediction models, and ability to measure, process and transmit turbulence information to FAA and pilots. Project leads will work large and small corporate firms to ensure commercialization of data and address new data as a supplement to current radiosonde data. Along with dramatic increase in observations above the PBL on a spatial (horizontal and vertical) and temporal scale, the new data could replace radiosonde data from balloons with substantial cost savings. Future commercialization could be provided via a service supported by a network of servers as well as with apps that enable companies to obtain and use the new data to prevent interference from winds, humidity, icing and turbulence. This new data will be very valuable to the airline industry as it will increase the amount of observations of wind speed, turbulence, icing, temperature and the conditions that can limit aircraft flight paths, impair shipping of products, and endanger passengers. It will also help with support to industries that depend on knowledge of the humidity and wind changes in the atmosphere that can impair optical sensors, LIDAR efficiency and ducting that can impact communication. There is also the potential for this data to greatly improve the forecasting of severe weather, winter storms and tropical storms that impact so many sectors of the economy.

Volatile organic compounds (VOCs) are emitted from a wide variety of biogenic and anthropogenic sources. Photochemical oxidation of VOCs creates ozone, a criteria pollutant. In addition, lower volatility oxygenated VOCs (OVOCs) produced from photochemistry can condense into fine particulate matter (PM). Both ozone and fine PM harm human health and can influence the Earth’s radiative balance. Reducing ozone and PM requires a detailed understanding of the emission and removal of VOCs at sufficient time and spatial resolution. Measurements of individual VOC and OVOC molecules are needed to confirm and validate model descriptions of ozone production and VOC fates and to validate satellite measurements. Progress in this area is limited by the lack of field-deployable and rugged monitoring instrumentation providing detailed molecular information. Atmospheric VOCs have traditionally been measured by collecting off-line samples in canisters or on adsorbent cartridges, followed by laboratory analysis with gas chromatographic (GC)-mass spectrometry (MS), GC-flame ionization detection (FID), or GC- photoionization detection (PID). However, these methods produce low time resolution (hours to days) and poor spatial coverage, as well as being subject to artefacts due to sample storage and handling. NOAA needs automated, high spatial and temporal resolution VOC measurements with low latency in order to improve characterization and forecasting of ozone and PM to improve public health. Automated in-situ or remote-sensing instrumentation that is compact, low-cost, and field-deployable on the ground or in aircraft would address the critical need for speciated and quantitative trace gas and VOC measurements. We anticipate that this instrument will yield significant direct commercial sales in the atmospheric science and environmental pollution monitoring and forecasting communities. Other applications could include biomedical research, pharmaceutical development, drug analysis, food and flavor industrial analysis, homeland security, and forensics.

This subtopic directly addresses the Department of Commerce’s strategic objective to “Reduce Extreme Weather Impacts - develop and deploy next–generation observation, data assimilation/processing, and modeling for the environment in order to make informed planning, resources management and investment decisions." NOAA and other federal agencies have been maintaining extensive observation networks and developing a large number of integrated earth prediction system models. Computer models developed for weather prediction, coastal ocean circulation, waves, and ice, as well as satellite remote sensing data are all computationally intensive, requiring high performance computing (HPC) to perform simulations, analysis and forecasts. An attractive alternative is to apply machine learning (ML), deep learning (DL), artificial intelligence (AI), pattern- recognition or data analytic approaches to improve the accuracy of earth prediction systems and the efficiency of automated data processing, pattern recognition and feature extraction from large volumes of datasets. Using these new techniques to improve efficiency and accuracy of the forecast products will potentially lead to earlier warnings for extreme weather and water events which have the potential to save more lives and reduce property damage. This call invites small, high-tech firms specializing in developing novel machine learning, artificial intelligence and pattern recognition algorithms to analyze and process large volumes of computer model results and satellite imagery in order to improve the efficiency and accuracy of integrated earth prediction systems (numerical weather prediction, ocean circulation, hydrological, waves, and ice modeling systems). The ultimate goal is to develop the next-generation of commercial applications, products and services in Information Technology (IT), autonomous vehicles, medical, insurance industries by applying machine learning or artificial intelligence technologies. Federal government agencies such as NOAA, USGS, DoD and DoE will no doubt benefit from such innovative technology, the true commercialization applications has much broader potential opportunities in multiple industries and market place.

With the technology advances in UAS systems, which includes sensors and platforms, it is of great interest to organizations that need to collect snow water data to fully exploit the unique capabilities of UAS. These organizations will benefit from the commercialization of research and technology development to enable UAS measurements of erosion processes and snow water for flood prediction and water management. Springtime flooding caused dramatic and expensive impacts in the US in 2019 throughout the eastern Great Plains and Midwest. Management of these impacts required significant data gathering and coordination between local, regional, and federal agencies. Spring flooding can be caused by a variety of mechanisms, but information about the winter snowpack is one of the greatest uncertainties. Erosion caused by high water, extreme events, and simply natural movement of waterways can occur during springtime as well as throughout the wet season. UAS applications have potential to provide valuable insight for this phenomenon for local, regional, and federal agencies. This project seeks to develop UAS sensors that can be used to measure snow water equivalent. Currently, the NWS National Operational Hydrologic Remote Sensing Center (NOHRSC) provides comprehensive snow observations, analyses, data sets and map products for the Nation (https://www.nohrsc.noaa.gov/). The NOHRSC measures snow water equivalent and soil moisture using gamma radiation remote sensing. This unique observing system includes two low-flying aircraft to conduct surveys in 31 states, including Alaska, as well as in 8 Canadian provinces. These data are incorporated into the National Snow Analyses. Although some UAS sensors already exist that collect data on snowpack properties, little published research has demonstrated their technological readiness level or effectiveness for operations. Erosion from natural changes in river geomorphology as well as driven by floods can also have devastating impacts on communities and infrastructure. Projects that use UAS- borne remote sensing or a combination of remote-sensing and modeling to derive information on snowpack properties such as snow water equivalent are encouraged.