Many disciplines within the geosciences require computational skills to access, analyze, and visualize data. These are skills students need to be competitive in the work environment. Applying computational thinking and basic coding into the classroom can diversify student learning, develop 21st century skills, and demonstrate real world applications through project based learning. Teaching students to have a broad base of knowledge and a range of skills is paramount in developing career ready-students. Working in collaboration with members of the Earth Science Information Partners (ESIP) Education committee, education professionals from UNAVCO, NOAA, Cooperative Institute for Mesoscale Meteorological Studies and teachers from schools around the country are exploring the use of basic coding and programmable robots as a springboard to learn computational thinking and skills within an Earth science context. By encouraging teachers to learn how to code, we help them to encourage their students to be creators, more than just consumers, of the technology around us and to foster curiosity that whets their appetites to learn more!. This presentation will elaborate upon coding in the classroom initiatives these partners are facilitating from workshops, learning materials, and insights from workshop feedback.

Various teaching methods including classroom lectures, physical experiments, and field excursions are useful for students to learn and understand the basic concepts of geography and earth sciences. However, due to constraints in the current curriculum of geographic education in Japanese schools, physical experiments and field excursions are rarely conducted, and classroom lectures tend to focus on memorizing some technical terms. This environment is not ideal for teaching processes and mechanisms of geographic phenomena. High-frequency, high-definition topographic data obtained using a TLS (Terrestrial Laser Scanner) and SfM-MVS photogrammetry with a UAS (Unmanned Aerial System) have become popular in geoscience. Those surveying approaches allow us to directly monitor rapidly changing landforms, while we can also use the obtained data to visualize geographic phenomena by various methods and materials including 3D print models, 3D virtual models, pictures, videos, and virtual/mixed reality. Here we explore the use of high-frequency, high-definition topographic data for educating geographic thinking. We arranged and conducted experimental teaching classes for elementary school students. First, we showed two 3D print models of the same sea cliff for years 2015 and 2017 constructed form high-definition topographic data. When students touched the two models, they were able to feel topographic changes due to erosion and sedimentation effectively. Furthermore, after exploring the 3D print models, many students were able to imagine how the sea cliff would change in the future. Next, we showed two images of fluvial deposits along a river segment in the area where the students live for July 2017 and September 2017. Then, they were able to imagine the transportation force of river flow. They also understood that the river flows typically in quiet but becomes powerful at high flow to move more sediment, and it might cause a disaster. Such visualized and touchable learning materials derived from high-frequency, high-definition topographic data enable students to enhance their geographic imagination of landforms, which are familiar to them but unexpectedly changing, at appropriate spatial and time scales.

A wide variety of research applications require knowledge of total solar irradiance (TSI) and solar spectral irradiance (SSI) on time scales from minutes to centuries. The current satellite data record of TSI and ultraviolet SSI is 40 years long while observations of solar irradiance at visible wavelengths through the near-infrared span 15 years. In late 2017, the NASA Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) mission was deployed on the International Space Station (ISS); these new TSI and SSI datasets are now extending the observational solar irradiance record with a planned 5-year mission. Recognizing the need for ongoing specification of solar irradiance, the National Centers for Environmental Information established the Solar Irradiance Climate Data Record (CDR) in 2014. The CDR includes a composite record of TSI observations and estimates of solar total and spectral irradiance variations during, and prior, to the space-based record based on the Naval Research Laboratory (NRL) models. Utilizing as inputs proxies of sunspot darkening and facular brightening, the models specify TSI and SSI annually since 1610 and daily since 1882. Both the observational composite and the model specifications are updated regularly and will eventually utilize the new TSIS-1 observations, both to extend the observational composite and to validate and improve the models. With the goal of establishing the utility of the NRL models in specifying the time and wavelength dependence of solar variability for the Solar Irradiance CDR, we compare the latest NRLTSI2 and NRLSSI2 modeled irradiances with observations, including composite records, and with independent models of solar irradiance variability. Our assessments quantify current understanding of solar irradiance variability on multiple timescales and identify areas where TSIS-1 observations are expected to provide improved understanding of solar irradiance variability. We use the following datasets in our comparisons: TSIS-1, Solar Radiation and Climate Experiment (SORCE), Ozone Monitoring Instrument (OMI), Solar Irradiance Data Exploitation (SOLID), Spectral and Total Irradiance Reconstructions for the Satellite Era (SATIRE-S), a three-dimensional extension of the SATIRE-S model (SATIRE-3D), and Empirical Irradiance Reconstruction (EMPIRE).

English is the accepted language of science, but the contexts in which climate change occur are not constrained to the English-speaking world. The languages of communities suffering from climate change range drastically, from regional dialects of popular languages such as Spanish to lesser known indigenous tongues. To conduct purposeful investigations on climate change in local knowledge systems, a researcher must be attentive to the language of the indigenous community. Critical terms for research and communication such as climate, weather, climate change and climate variability are what we might call “multifaceted” words. They do not translate perfectly from one language to another and can carry different implications and meanings that will skew outcomes if not carefully navigated and negotiated with local knowledge systems. Research and science communication require attention to, respect for and sensitivity to linguistic differences. I share recommended practices for research across language differences based on research and lessons garnered during fieldwork conducted on climate change and coffee farmers in Risaralda, Colombia, with an international team. These four practices for navigating language differences are: (1) Establish an understanding of what “multifaceted” words mean ahead of time and consult if necessary; (2) Establish a protocol for researchers to keep notes on various manifestations of or uses of particular concepts or terms in the local context; (3) Debrief with the team every day in order to rapidly adjust language of interview protocols or communications in response to local feedback; (4) Understand that if you have a foreign accent, your interviewees are tolerating you, not the other way around.

Modern bedrock streams and rivers emerged in the Midwestern United States in response to glacial outwash floods ~19,000 years ago and continued to adjust to new drainage patterns over the landscape formed by Wisconsin glaciation events. The Illinois River and corresponding tributaries form a fascinating landscape with canyons carved 50-200 m deep into the St. Peter Sandstone. These geomorphic features are preserved and open to visitors as Starved Rock State Park in Central Illinois. Free access to the state parks and close proximity to the large Chicago metropolitan area result in frequent visitation to this natural attraction. Recently, the rates of natural stream incision appear to be overprinted by direct human influences on the landscape. The St. Peter Sandstone is a weakly cemented, extremely friable quartz arenite, making it susceptible to human disturbance and rapid natural erosion. This study explores how quickly present day changes occur along bedrock surfaces using photogrammetric Structure-from-Motion (SfM). Repeat photographic data were collected once per month from two sites within the park where canyon walls contained human carvings that served as reference features to align photos and monitor change. Photos were merged in Agisoft PhotoScan Pro to construct 3D point clouds and imported into CloudCompare to measure changes to rock surfaces between monthly visits. The photogrammetric SfM analysis detected measurable change on a centimeter to millimeter scale. Changes along footpaths were observed when visitor traffic was high. During winter months when visitor traffic decreased, rainfall and snowmelt runoff primarily caused mobilization and removal of loose sediments covering bedrock surfaces. Bedrock thin sections from each site were studied to assess the influence of cement on erosion rates. Lower cement concentrations were observed at the outcrop site with the greatest measured surface change. Changes detected with SfM analyses demonstrate that human interactions can influence erosion processes in a short time. While both natural and human caused changes occurred on bedrock surfaces, precipitation created greater measurable differences.

How will coastal forests respond to rising sea levels, disturbances such as storms, drought, and climate change, which are combining to cause widespread impacts on ecosystems at the terrestrial-aquatic interface? One particular area of uncertainty is how these processes may mobilize soil organic carbon, resulting in changes in the dissolved or gaseous greenhouse gas (GHG) fluxes. We present results from the first year of a manipulative experiment looking at the effects of changes in salinity exposure and water availability on soil GHG fluxes. Large (40 cm diameter) soil cores were transplanted along natural salinity and elevation gradients in a Maryland, USA, coastal forest subject to rapid sea level rise. Comparative observations were also made in a western Washington, USA watershed with slow sea level rise but strong tidal fluctuations. Disturbed and undisturbed control cores allow us to distinguish transplant from salinity effects; soil respiration measurements were made every 7-10 days. Cores transplanted to lower-salinity sites, and to higher-elevation plots, exhibited elevated GHG fluxes relative to disturbance controls. These preliminary results suggest that changes in salinity exposure and water availability may exert significant effects on coastal forest GHG fluxes and the stability of soil carbon.

Historical volcanic eruptions in Canary Islands have traditionally been considered as quiet eruptions, mostly effusive and with scant explosions (VEI 1-2), attending to the general characteristics of this volcanism. Nevertheless, the geological record of these eruptions shows abundant signs of moderately explosive activity with PDC desposits and bombs and blocks of considerable size outcropping in the islands. Scientific studies of Canarian historical volcanism have been focused on the analysis of the volcanic processes and the assessment of related hazards, paying special attention to the monitoring of volcanic activity in the last few years. However, there is hardly any data of their effects on the population, like number of victims, affected people, displaced, emigrated, damage to the crops, lands, animals, etc. In general, there is also no accurate information on the actions of authorities or on the crises management. The knowledge of the impacts caused by volcanic eruptions provides very significant information to be prepared for future eruptions, and can be found in documentary sources, both handwritten and printed and graphic, in official or private documentation. The analysis of these historical chronicles has shed light on these aspects not previously considered neither by the authorities nor the scientist or general public. The results of this research examining the historical chronicles shows that, contrary to what is generally believed, historical eruptions in Canary Islands did cause several impacts both on the population (dead, injured or missing, displaced and evacuated), and on the economy (damage to crops, livestock, buildings, roads and various infrastructures). The historical documents also highlight that there was indeed crisis management, both from the political and scientific authorities in most of the eruptions. From all these we can conclude that no social interpretation has been made about the legacy of historical eruptions in Canary Islands nor about the impact they had. We should implement this knowledge in the prevention and emergency plans made by the authorities for a better management of future eruptions, considering the population increase in the Canary Islands from ca. 360000 inhabitants in 1900 up to ca. 2.13 millions in 2018, plus some millions of tourists (15.5 in 2018).

Forest management can enhance forest resiliency against natural disturbances such as fire, drought, or disease. Mechanical thinning, followed by a prescribed burn, is a useful technique to achieve a desired forest structure, usually maximizing large tree basal area or decreasing fuel loads, meant to protect against wildfire or reduce water stress in the western US. Changing forest structure can alter ecosystem function by reducing competition and exposing soil, modifying microclimates and creating suitable conditions for shrubs and grasses to encroach. Typically, forest treatments are expected to make the remaining trees more productive through competitive release, and an open canopy helps the understory to thrive. This enhanced plant water use often contradicts the expected result of increased streamflow following thinning. In mountainous terrain, water yield is further complicated by hillslope-scale processes of subsurface lateral flow and groundwater recharge. This research seeks to understand how management-derived forest structure influences hillslope-scale forest regrowth and water yield. We apply a spatially-distributed ecohydrologic model (RHESSys) to an experimental hillslope in the Sierra Nevada, CA. We incorporate multi-temporal Lidar observations and U.S. Forest Service Forest Inventory & Analysis (FIA) survey data to estimate post-thinning regrowth in treated plots in the watershed, which is used to verify RHESSys accuracy of vegetation regrowth. Then, we run long-term virtual thinning experiments to understand how the combination of thinning and prescribed burns in upslope and riparian sites separately and concurrently influences regrowth and water fluxes in these sites. We expect that an intermediate forest density will yield the most co-benefits in terms of carbon sequestration and water yield. However, these patterns will likely be modified along a hillslope, such that riparian forest stands will be less sensitive to the competitive release that thinning provides, whereas dense upslope forests will be highly sensitive to treatment since they are more water-limited. Water yield is likely to be confounded by multiple factors, including topography, whether a burn follows thinning to remove understory fluxes, and interactions between upslope thinning and processes of lateral flow and groundwater recharge when increased riparian water use compensates for additional upslope subsidies.

The Seismometer to Investigate Ice and Ocean Structure (SIIOS) project is exploring the science capabilities of seismometers in ocean world analog environments. Ocean worlds, such as Europa, Enceladus and Titan, have thick global icy shells overlying liquid oceans. The icy shells may be seismically active due to tidal stresses. SIIOS tests several seismometers in a small-aperture array in a mock-lander configuration to quantify the ability to detect, locate, and identify seismic sources, as well as constrain local ice structure. The SIIOS experiment was deployed on two terrestrial analogs for ocean worlds. We first deployed on Gulkana Glacier in Alaska in September 2017, and then deployed in Northwestern Greenland, over a subglacial lake from May 2018-August 2018. Both areas serve as analog locations for Europa due to the layering of ice, water and rock. Gulkana was a relatively noisy site due to surface runoff and drainage, higher topographic variation (inducing rockfalls), and proximity to active plate boundaries. Greenland was a quieter site, in part due to its geologic setting high on the ice sheet, as well as from the installation process. During the Greenland deployment, we covered instruments with a large aluminum box that was buried, thus reducing noise from atmospheric and thermal effects. At both analog sites, the instruments passively recorded seismicity and seismic background noise. The passive data was used to create power spectral density (PSDs) and then probability density functions (PDFs), of the background noise. The PDFs of Gulkana showed higher noise levels compared to those of Greenland. Using the passive data, we detected and identified events originating from ice quakes, and in the case of Gulkana; rockfalls and drainage from a nearby moulin. A frequency-dependent polarization analysis was also conducted to indicate the dominant directionality of the background signals through time. The results indicate how background or ambient signals could be used on ocean worlds to characterize the local seismicity.

Climate change is both a physical and social phenomenon in which individual understandings are contextualized within broader considerations: individuals are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Personal experience, local knowledge, and social-learning influence climate risk perception and vary substantially among countries and regions. Likewise, they differently affect individuals and social groups at the regional and local scale, among whom exposures, attitudes, and capacities to manage risks vary greatly. A climate storyline approach is hence well-suited to study human observations, compound climate risks, and inform and conceptualize human–water systems interactions. Narrative storylines are used as input drivers to climate models, to represent different development pathways, which are usually characterized and applied at national and sub-national scales. Storylines aim to provide new social scenarios that address local human cognition uncertainties and improve human behavior modelling and robustness when addressing decision-making processes. Climate risks and hazards understanding can be communicated by presenting the experiences or a sequence of events, facts, and observations that are plausible and potentially critical for the system under study. Methods guiding storytelling are usually focused on conducting interviews with stakeholders, carrying out collective workshops, developing appropriate focal questions, and iterating between model results and key stakeholders. Therefore, can other data collection tools be used to reduce uncertainty in physical aspects of climate change from individuals’ local experience and perception? This contribution presents a triple-loop survey to detail the core elements of farmers’ perception and behavior when addressing climate change risk. We collect first-hand observations from northern Italian farmers about how climate change affects their activity and how extreme events are conditioning their adaptation capacity. Emphasis is placed on understanding the driving factors (risk awareness, perceived impacts, and adaptation measures and barriers) involved in the physically self-consistent past events and the plausibility of those factors. Moreover, we want to test if these factors can provide relevant implications for appropriately modelling storylines in decision-making processes. Tentative results can be useful to discuss the methodological framework of storylines building and narratives modelling, and at which point surveys can be an alternative and complementary way of dealing with deep uncertainty within climate risk management and social scenarios modelling.

Climate change is arguably the most severe and complex challenge facing today’s society, a cross-cutting issue affecting many sectors and connected to other global challenges, such as ensuring sustainable water management and food security. Agricultural systems are adversely influenced by climate change through increased water stress, change in run-off patterns, seasonality fluctuation, and temperature variations. Farmers are, hence, a valuable source of first-hand observations of climate change as they may provide a deeper understanding of their manifestation, relevance, and effects. Social and behavioural sciences have investigated the influence of farmers’ experiences in increasing climate change adaptation capability and improving decision-making processes at the system level. The conclusion is that local perceptions provide sufficient baseline information for understanding individual and collective exposure to climate risks, an essential element for effective policy formulation and implementation. Traditional management approaches based on simple, linear growth optimization strategies, overseen by command-and-control policies, have proven inadequate for effective adaptation to climate change. Conversely, accurate bottom-up approaches focused on social learning can complement the system transformation by building collaborative problem solving among individuals, stakeholders, and decision-makers. In this context, deepening social perception becomes fundamental for two main reasons: i) it is a key component of the socio-political context, and ii) it is an essential step for behaviour transformation and attitude change. In this line, associative processing methods, such as interviews and surveys, have been discussed for their ability to monitor the nature, extent, significance, and influence of personal experience on climate change adaptation. Also, modelling techniques have been recognized in social sciences as effective mechanisms to simulate the social influence in decision-making processes. System dynamics (e.g., causal loop diagrams, CLD) and Agent-Based Models (ABM) can include feedback between social and physical environments, define individuals’ and stakeholders’ narratives, and map the social network with agents’ interactions. This proposal aims at testing how qualitative data can enable policy-makers and managers to understand and re-think water management and climate change policies at the local level, which is essential to address agricultural risks. From a system dynamics approach, we examine how ABMs can most effectively integrate behavioural data collected from semi-structured interviews and surveys to increase robustness in decision-making processes while attending to farmers’ behaviour on climate change adaptation. We surveyed 460 farmers and semi-structured interviews with 13 irrigation consortiums from northern Italy to deepen a triple loop analysis on climate change awareness, perceived impacts, and adaptive capacity.

Amazon rainforest has been subject to intensive deforestation in the last decades, for example, illegal logging and creating pasture areas. A characteristic pattern of deforestation seen from space is the “fishbone” shape, which usually appears near roads, rivers and its tributaries. Indeed, others, more subtle, still need to be identified. These fishbone images are spatiotemporal patterns that need to be more explored with feature extraction methods. In computer vision, morphological features such as flatness, compactness, circularity, perimeter, area, and centroid are well-known to characterize the appearance of an object. In this work, we aim to characterize the shapes of deforestation in its early stages and its evolution in time, particularly in the Amazon rainforest. Thus, we propose to analyze satellite images of these regions to crop and segment by using shape features.

Identifying subsurface contamination is challenging as the sources are not directly perceivable. Aquifer contamination only gets noticed when it is measured in one of the observation wells. As remediation of the contaminated sites is expensive and time-consuming, it is essential to locate sources of contamination for efficient remediation design and water resources management. Simulation-optimization approach is popularly used for identification of contaminant sources. However, the numerous runs required for the simulation model by utilizing the optimization algorithm makes this approach computationally expensive. Alternatively, the simulation model can be substituted by a surrogate model which can significantly reduce the computational cost. In this study, a deep neural network (DNN) based surrogate model is proposed for simulating the transport of a reactive contaminant Tritium in a hypothetical aquifer. The DNN is trained by considering injection rates at possible source locations as inputs and concentration at observation wells simulated by meshfree Radial Point Collocation Method (RPCM) as output. RPCM efficiently handles instabilities associated with advection and reaction dominant problems in comparison to grid/mesh-based methods. The backpropagation approach is used to optimize the weights and biases of the DNN using adaptive moment estimation (ADAM) as an optimizer. The performance evaluation of the surrogate model yields Mean Squared Error (MSE) close to zero and correlation coefficient (R2) of 0.99. An inverse model is developed by linking the DNN surrogate model and Particle Swarm Optimizer (PSO). The application of the inverse model show that the DNN-PSO model can predict the injection rates at possible source locations accurately.

Remote sensing approaches based on VIS-NIR spectroscopy can be used for getting near real-time information about soil fertility. However, the main challenge limiting the application of spectroscopy in soil fertility evaluation is finding suitable data pre-processing and calibration strategies. We have compared various pre-processing techniques using the reflectance spectra obtained from AVIRIS-NG hyperspectral images, for quantification of organic carbon (OC), available phosphorus (P) and available potassium (K) in the surface soils of Surendranagar area (Western parts of India) and Raichur (Southern parts of India). Surface (0 - 0.15 m) soil samples were collected from these two areas synchronously with the dates of the AVIRIS-NG campaign. The soil samples were air dried, sieved to <2 mm, and analyzed for OC, P, and K using standard methods. The AVIRIS spectra (spectral range of 380-2500 nm with an interval of 5 nm) corresponding to soil sampling points were extracted. The pre-processing steps were used in the order: Continuum Removal (Yes/No), Moving Window Abstraction (Yes/No), No transformation or Euclidean Normalization or Standard Normal Variate (SNV), No transformation or Savitsky-Golay (SG) first-order smoothing, and No transformation or first derivative OR second derivative. We have used the partial least squares regression (PLSR) to calibrate the model from pre-processed spectra. The PLSR with Continuum Removal, SNV, SG first-order smoothing, and first derivative was selected as the best algorithm for estimating soil properties from the Western parts of India, and the corresponding R2 were 0.77 for OC, 0.79 for P and 0.83 for K (RMSE <0.3 for all the parameters). The PLSR with Moving Window Abstraction, SG first-order smoothing, and second derivative were selected as the best algorithm for estimating soil properties from the Southern parts of India, and the corresponding R2 were 0.54 for OC, 0.49 for P and 0.56 for K (RMSE <0.3 for all the parameters). These results suggest that the optimization of AVIRIS spectra using various pre-processing techniques and modeling approaches is required for rapid and non-destructive assessment and monitoring of soil health for precision agriculture.

Polygonal Fault Systems (PFS) are an enigmatic class of small non-tectonic extensional faults. PFS are predominantly hosted in fine-grained sedimentary tiers and are prevalent along many continental margin basins. The genesis of PFS is widely debated and little is known about the timeframe for polygonal fault growth. We present the first measurements of throw rates for polygonal faults by measuring the vertical offset of seven age-calibrated horizons mapped using three-dimensional seismic reflection data from the Norwegian Sea. Individual polygonal faults exhibit a range of throw rate profiles through time, ranging from near linear to singly or multiply stepped. The stepped profiles have a range of short-term throw rates ranging from 0 to 18 m/Ma. Time-averaged throw rates of 180 polygonal faults over the entire 2.61-0 Ma interval are normally distributed and range between 1.4-10.9 m/Ma. We convert our PFS throw rates to displacement rates and compare these to published displacement rates for gravity driven and tectonic normal faults. We find that the displacement rates of polygonal faults mark the lower limit of a continuous spectrum of extensional fault displacement rates; they are up to two orders-of-magnitude slower than gravity-driven faults, and up to three orders-of-magnitude slower than the fastest growing tectonic faults. We attribute the ultra-slow kinematic behaviour to the non-tectonic nature of polygonal faults where throw accumulates primarily through dewatering of the largely fine-grained sediments comprising the host layers for the PFS, and differential volumetric strain between the fault footwalls and hangingwalls.

Being an agent of environmental hazard, wild fire is a major concern, especially in Mediterranean countries, where warm and dry summers may lead to high levels of vegetation stress. Vegetation fire, whether caused naturally or by humans, its widespread and occurrence depends on land cover (LC) type and state, climate and meteorological conditions, land use. The main concern of the study is quantification the physical processes related to vulnerability of biomass burning across the climatic gradient for Eastern Mediterranean Europe (Bulgaria) using satellite information, ground observations and model outputs for land surface state. Sixteen years (2004-2019) satellite data from LSASAF FRP-PIXEL product at MSG resolution were processed in order to determine the distribution of vegetation fires for the most risky period July-September. The work examines the relationships of wildfire statistics (number of fires and radiative energy released) to surface temperature (according LSASAF LST product), and soil moisture availability, SMA (according SVAT model) as energy-moisture related climate variables. Regression analyses for the main aggregated vegetation types (forest, shrubs, cultivated) reveal statistically significant dependences: - In short-term climatic aspect: a high correlation between monthly mean positive LST anomalies and fire activity as well as negative correlation between SMA anomalies and fire activity; - In long-term climatic aspect: the contribution of different LC types for fire activity is considered. Identified specific fire regime with location of spots with highest fire activity provides knowledge about fire-climate relations on a regional scale, which is important to define more risky regions as well as for projection the effects of global environmental change.

The Ocean City Inlet was created in the early 1930s by a strong hurricane mobilizing the sediment and thus separating Ocean City from Assateague Island, making the Maryland coastal bay a two-inlet system, with Chincoteague Inlet 60 km to the south. Ecological and hydrodynamic impacts of tropical storms on coastal regions have been well-documented. However, little work has been conducted comparing the impacts of extratropical winter storms with hurricanes, specifically at two-inlet systems. Observed winds, water levels, and waves from NOAA’s National Data Buoy Center (NDBC) and predicted data from the North American Regional Reanalysis (NARR) and Finite Volume Community Ocean Model (FVCOM) models were compiled for 2016 to 2018 to compare the magnitude of forces driving volumetric flow during tropical and extratropical storms. Although the inlets are around 60 km apart, the water level responses were similar during the three blizzard events examined. During each blizzard, the water level increased at the initial arrival of the low-pressure system and then decreased at both inlets over the course of the storm, potentially owing to sustained wind pushing water out of the inlets. The wind and wave forcing on both inlets will be compared using field data to validate modeled data for one blizzard and hurricane per each year. This study, along with the validated hydrodynamic models that were utilized, will assist in predicting environmental stressors and potential influences on shoreline zones under varying storm intensities in two-inlet systems.

Over the past decade, the huge success in many large-scale projects like the USArray component of Earthscope gave rise to a massive increase in the data volume available to the seismology community. We assert that the software infrastructure of the field has not kept up with parallel developments in ‘big data’ sciences. As a step towards enabling research at the extreme scale to more of the seismology community, we are developing a new framework for seismic data processing and management we call Massive Parallel Analysis System for Seismologists (MsPASS). MsPASS leverages several existing technologies: (1) Spark as the scalable parallel processing framework, (2) MongoDB as the flexible database system, and (3) Docker and Singularity as the containerized virtual environment. The core of the system builds on a rewrite of the SEISPP package to implement wrappers around the widely accepted ObsPy toolkit. The wrappers automate many database operations and provide a mechanism to automatically save the processing history and provide a mechanism for reproducibility. The synthesis of these components can provide flexibility to adapt to a wide range of data processing workflows. The use of containers enables the deployment to a wide range of computing platforms without requiring intervention by system administrators. We evaluate the effectiveness of the system with a deconvolution processing workflow applied to USArray data. Through extensive documentation and examples, we aim to make this system a sustainable, open-source framework for the community.