The 3D Printed Magnetosphere Project is a collaboration between Aurorasaurus and the NASA STEAM Innovation Lab, both partners of the NASA Space Science Education Consortium (NSSEC). The Earth’s magnetosphere is a complex, multifaceted, and intangible system that poses unique challenges to science communication and education. Two-dimensional diagrams inherently oversimplify its structure and processes, leading to misunderstood or incomplete understandings of the physics involved. In addition, diagrams lack tactile accessibility, excluding some learners. While three-dimensional tactile models with nested components are classic tools for illustrating biological and geophysical concepts, similar models have not yet been created for the magnetosphere. This project is an effort to create the first physical, open-source, customizable, three-dimensional, and 3D-printed model of the magnetosphere. We provide a NASA STEAM Lab Exploration Idea Profile detailing the current scope and future potential for the product. Our preliminary model is intended to provide a starting template that illustrates the following basic structures: the magnetosheath; an equatorial cross-section; a torus representing the outer radiation belt; the ring current; and Earth, including the crust, mantles, core, and aurora. The magnetosheath will be hinged and open on the x-axis like a case or shell, revealing the other structures nested inside. The components will be removable, and the radiation belt and Earth will have the capability of opening to reveal interior structures. The printable model will be shared with the Maker community, enabling customization to illustrate specific concepts, add classroom features, and provide tactile accessibility for learners with low vision. In addition, crowdsourced expertise from the space physics and Maker communities will contribute greatly to further refinements. This presentation will provide an overview of the model and explore its potential applications. These could include better contextualizing not only physics concepts, but missions like the NASA Magnetospheric Multiscale Mission (MMS) launched in 2015 to study the Earth’s magnetosphere, using four identical spacecraft flying in a tetrahedral formation. It is currently exploring magnetic reconnection, one of the mechanisms that causes aurora.

Nitrogen is the most abundant element in the Earth’s atmosphere. Around 78% N2; and 21% O2; form the air we breathe and expand into high-altitude atmosphere, the thermosphere, and eventually the ionosphere. The neutral molecules in the ionosphere are ionized by solar radiation, and some of them break up into atoms, and others become charged particles. The ionospheric ions with sufficient energy can flow out into space, and the abundances of these outflowing ionospheric ions highly impact the near-Earth plasma properties. Studies focused on outflowing O+ ions have been conducted for many years. However, the contribution of N+ to the outflow solution is still largely unknown due to the instrumental limitations. We developed a first-principled physics model to understand how N+ and molecular ions, including NO+, N2+, and O2+, acquire the sufficient energy to escape Earth’s atmosphere. This study reveals the importance of N+ ions in the high-altitude polar ionosphere, from few hundred kilometers to thousands of kilometers in the space, and examines the possible mechanisms to accelerate and removed them from Earth’s atmosphere.

The retrieval of atmospheric composition from remote sensing measurements is a complex process that requires the integration of cross cutting domain knowledge into a coherent software package. The complexity is increased many times over when the software has to handle multiple types of instruments, operating in different spectral regions, each with their own peculiarities. This is further compounded when trying to combine information from multiple instruments for joint retrievals. Yet, there is enough overlap between the radiative transfer and retrieval techniques used by various missions that it is wasteful to continually reinvent the wheel every time. The Reusable Framework for Atmospheric Composition (ReFRACtor) is an extensible multi-instrument atmospheric composition retrieval framework that supports and facilitates data fusion of radiance measurements from different instruments in the ultraviolet, visible, near- and thermal-infrared. This framework is being developed to provide a community available software package that uses robust software engineering practices with well tested, community accepted algorithms and techniques. ReFRACtor is geared not only for the creation of end to end production systems, but also towards independent investigative scientists who need a software package to help answer atmospheric composition questions. We will explain how the use of succint interfaces between components provides advantages for future proofing, flexibility and reusability. Examples will be given for translating the logical separation of mathematical and scientific concepts into software components. We will describe how having a Python interface to fast compiled algorithms is helpful for rapid prototyping of new systems. The experience of early adopter scientists will also be discussed to give a perspective from outside the software development team.

Subsurface characterization is important for the detection and development of underground resources. Useful subsurface models can correctly make production forecast and optimize development plans, such as infill well location in an aquifer, geothermal, and hydrocarbon reservoir development. A useful subsurface model should honor geological concepts as well as all the available measurements, such as fluid production history, geophysical measurements. However, common subsurface modeling methods cannot efficiently honor the two concepts. To build subsurface models in a way that it is easy to condition them to both geological concepts and available measurements, we develop a new machine learning method, referred to as the stochastic pix2pix method. In this method, we use convolutional neural networks and adversarial neural networks to stochastically generate new subsurface models matching both geological concepts and static measurements, such as seismic and well data. This method first extracts the depositional patterns from analog training images, such as outcrops, high-resolution seismic images, and depositional process-based reservoir models, and then minimizes the Jensen-Shannon entropy between the training images and new subsurface models, as well as the mismatch of static measurements. The hydraulic inverse problem is solved with a machine learning-based proxy model on the model parameter space defined by stochastic pix2pix. The stochastic pix2pix method helps maintain the match of geological concepts and static measurements during the inversion. To verify and benchmark our procedure, we show the conditional subsurface models generated with stochastic pix2pix reproduce the geological concepts as good as synthetic unconditional process-based models. we successfully build reservoir models for channel and turbidite fan systems, where the depositional patterns of common geobodies are well reproduced. The synthetic well data, seismic interpretation, net-to-gross ratio, and time records of fluid production are well-matched with this new method. Additionally, we generate conditional subsurface models 90% faster than with conventional object-based modeling methods and with more accurate reproductions of the available measurements.

Declining sea ice in the Arctic Ocean is exposing its coasts to more frequent and intense forms of wave energy and storm surge. As a result, erosion rates along some stretches of coastline in the Alaskan Arctic have doubled since the middle of the 20th century and now rank among the highest in the world. People concentrated near the coast are being heavily impacted by erosion, with some facing relocation. Coastal erosion is projected to increase the cost of maintaining infrastructure by billions of dollars in the coming decades. The financial impact of enhanced erosion will likely be further exacerbated by emerging geopolitical pressures, including the discovery of natural resources, opening of new shipping routes, and construction of support facilities in the Arctic. Scientific knowledge and engineering tools for predicting coastal erosion and guiding land-use decision are not well-suited for the ice-bonded bluffs of the Alaskan Arctic. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is thus needed. We introduce a geomechanical simulation framework, informed by field observation and laboratory testing, that focuses on the impact of bluff geometry and material variability on permafrost bluff stress states associated with a 9-km stretch of Alaskan Arctic coastline fronting the Beaufort Sea that is prone to toppling-mode block failure. Our approach is advantageous in that it is based on measurable physical properties (e.g., the bluff geometry, permafrost bulk density, Young’s Modulus, and Poisson’s Ratio) and does not require the potential failure to be defined a priori, but rather, the failure area can be interpreted from the multidimensional patterns of stress produced by the model. Our findings highlight how (1) block failure characteristics could be tied to variations in the intensity and duration of the storm energy that intersects the coastline and (2) how deformation processes that create non-uniform patterns of displacement may play a role in localizing block failure. We propose that this kind of physics-based simulation approach can facilitate hypothesis testing regarding the prediction of decadal-scale erosion rates for increasingly dynamic coastal permafrost systems.

Sunlight plays an important role in regulating the nutrient cycle in streams. Throughout the years, more streams are buried to accommodate for residential or commercial facilities. This results in severe impacts on the health of streams due to altered exposure to sunlight, air, and soil; it subsequently affects the growth of aquatic and riparian vegetation, thus impairing the water quality of streams. Although urbanization effects on urban surface streams are well understood, the adverse effects of burying streams on dissolved oxygen (DO), reaeration, photosynthesis and respiration processes have been mainly remained theoretical. This study evaluated the effect of stream piping on DO for two sections of Stroubles Creek in Blacksburg, VA through comparing the water quality prior to entering, and post exiting the buried sections of the Creek. Monitoring DO was conducted through manual readings and continuous measurement for a number of days in Fall 2015 and 2016. The results indicated that the water DO level decreased by approximately 11% as water flowed through the buried sections. The covered section of the stream also caused variability of the water temperature, resulting a possible disturbance to the downstream ecosystem. The buried channels-induced DO variations effects on the Brook Trout (Salvelinus sp.), a sensitive species present at the Stroubles Creek headwaters was also assessed. Further, the photosynthesis rate, reaeration coefficient, and daily respiration rate were computed prior and after one of buried channels through a series of mathematical and empirical approaches to assess the buried channels effect on the aforementioned parameters. The photosynthesis and respiration rates were reduced at the inlet compared to outlet, primarily due to less solar radiation and aquatic vegetation oxygen uptake, however, reaeration rate was increased at the outlet compared to inlet. This study can assist the decision makers and resource planners in taking the appropriate decisions regarding daylighting stream channels to improve water quality or/and accommodate increasing demand for urbanization.

The devastating impacts of the widespread flooding and landsliding in Puerto Rico following the September 2017 landfall of Hurricane Maria highlight the enhanced hazard potential from increasingly extreme storms in mountainous humid-tropical climate zones. Long-standing conceptual models for hydrologically driven hazards in Puerto Rico posit that hillslope soils remain wet throughout the year and antecedent soil wetness imposes a negligible effect on hazard potential. Our post-Maria in situ hillslope hydrologic observations indicate that while some slopes remain wet throughout the year, others exhibit appreciable seasonal and intra-storm subsurface drainage. Therefore, we used receiver-operating characteristic analysis and the Threat Score (TS) skill statistic to evaluate the performance of hydro-meteorological (soil wetness and rainfall) versus intensity-duration (rainfall only) hillslope hydrologic response thresholds that identify the onset of positive pore-water pressure, a predisposing factor for widespread slope instability in this region. We found that the hydro-meteorological thresholds outperformed intensity-duration thresholds for a seasonally wet, coarse-grained soil (TS = 0.8 vs. 0.6, respectively), although they did not outperform intensity-duration thresholds for a perennially wet, fine-grained soil (TS = 0.2 vs. 0.2, respectively). These soils types may also produce radically different stormflow responses, with subsurface flow being more common for the coarse-grained soils underlain by intrusive rocks versus infiltration excess and/or saturation excess for the fine-grained soils underlain by volcaniclastic rocks. We conclude that variability in soil-hydraulic properties, as opposed to the humid-tropical climate zone, is the dominant factor that controls runoff generation and modulates the importance of antecedent soil wetness for our hillslope hydrologic response thresholds. Our findings encourage further deployment of continuous in situ hydrologic monitoring to facilitate the development of empirical hillslope hydrologic response and landslide thresholds for regional-scale hazard warning systems that must account for spatially variable soil types.

Landcover information is of relevance for a range of applications in Arctic environments including up-scaling of in situ measurements, permafrost monitoring and modelling and climate change impact assessment. But not only natural landcover types, also the identification of human impacted areas is required. A wide range of satellite records are available, but often there are coverage and resolution limitations. Due to the spatial heterogeneity typical for the Arctic, an as high as possible spatial resolution is needed. Sentinel-1 as well as Sentinel-2 offer 10m nominal resolution for specific bands and modes and are freely available across the entire Arctic. The multi-spectral information from Sentinel-2 is specifically of value for discrimination of tundra types. The Synthetic Aperture Radar mission Sentinel-1 provides added value through representation of land surface structure features. A combination of both allows significantly improved characterization of landcover over larger areas. Here, we review recent progress in monitoring the land surface close to Arctic coasts (focus region of HORIZON2020 Nunataryuk), specifically the distribution of human impacted areas beyond of what is represented in databases such as OpenStreetMap or global settlement maps. The added value of machine learning techniques will be discussed and results based on Sentinel-1 und Sentinel-2 presented. Further on, permafrost change information provided through the ESA CCI+ Permafrost project is combined with the novel maps. The CCI+ Permafrost datasets cover 1997-2018 for ground temperature, active layer thickness and permafrost fraction with 1 km gridding (partially derived from MODIS LST, https://climate.esa.int/en/projects/permafrost/data/) and therefore allow detailed assessment of areas with human presence over permafrost.

The Surface Biology and Geology global imaging spectrometer is primarily designed to observe the chemical fingerprint of the Earth’s surface. However imaging spectroscopy across the visible to shortwave infrared (VSWIR) can also provide important atmospheric observations of methane point sources, highly concentrated emissions from energy, waste management and livestock operations. Relating these point-source observations to greenhouse gas inventories and coarser, regional methane observations from sensors like the European Space Agency (ESA) TROPOMI will contribute to reducing uncertainties in local, regional and global carbon budgets. We present the Multi-scale Methane Analytic Framework (M2AF) that facilitates disentangling confounding processes by streamlining analysis of cross-scale, multi-sensor methane observations across three key, overlapping spatial scales: 1) global to regional scale, 2) regional to local scale, and 3) facility (point source scale). M2AF is an information system that bridges methane research and applied science by integrating tiered observations of methane from surface measurements, airborne sensors and satellite. Reducing uncertainty in methane fluxes with multi-scale analyses can improve carbon accounting and attribution which is valuable to both formulation and verification of mitigation actions. M2AF lays the foundation for extending existing methane analysis systems beyond their current experimental states, reducing latency and cost of methane data analysis and improving accessibility by researchers and decision makers. M2AF leverages the NASA Methane Source Finder (MSF), the NASA Science Data Analytics Platform (SDAP), Amazon Web Services (AWS) and two supercomputers for fast, on-demand analytics of cross-scale, integrated, quality-controlled methane flux estimates.

The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC; formerly GeoSPEC) pioneers an on-demand science data processing system (SDPS) producing user-customized Level 1 calibrated radiance to Level 3+ data products in anticipation for the 2017-2027 Earth Decadal Survey prioritized spaceborne global imaging spectrometer to advance the study of Surface Biology and Geology (SBG). SBG data volumes (~20 TB/day) of high dimensionality (>224 bands) would be infeasible to download and the breadth of applications of the data across dozens of disciplines presents a need to evolve the traditional NASA SDPS. ImgSPEC streamlines processing data into key SBG observables that have demonstrated algorithms at local-to-regional scales and may vary locally. As such, a traditional, monolithic SDPS could not fully exploit the information in SBG measurements. To remove this barrier to use, ImgSPEC demonstrates an on-demand SDPS prototype that improves imaging spectroscopy data discovery, access, and utility enabling shared knowledge transfer from advanced imaging spectroscopy users to less experienced users such as decision makers and the general public. We test three use cases: 1) standard data processing workflows, 2) customized variants of standard workflows, and 3) algorithm development of new workflows. We create collaborative algorithm development environments that offer services typically restricted to NASA SDPSs such as data product provenance and bulk processing. We leverage existing NASA-funded information technologies such as the hybrid on-premise/ cloud science data system (HySDS), the Multi-mission Algorithm and Analysis Platform (MAAP), ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the SBG Science and Applications Communities.

The CYGNSS constellation of eight satellites was successfully launched in December 2016 into a low inclination (tropical) Earth orbit. Each satellite carries a four-channel bistatic radar receiver which measures signals transmitted by Global Positioning System (GPS) satellites and scattered back into space by the Earth surface. Over the ocean, surface roughness, near-surface wind speed and air-sea latent heat flux are estimated from the surface scattering cross section. Over the land, estimates of near-surface soil moisture and imaging of inland water bodies and flood inundation are derived from the surface reflectivity. The measurements are able to penetrate through all levels of precipitation and through most vegetation canopies due to the long radio wavelength at which GPS operates. The number of satellites in the constellation and their continuous data-taking operation produces high spatial sampling density and low temporal revisit times. Over ocean, this makes possible the reliable detection of tropical cyclone intensification and the resolution of diurnal cycles in tropical winds. Over land, diurnal soil moisture variability is resolved and rapidly changing flood inundation events are mapped. Engineering commissioning of the constellation was completed in March 2017 and the mission is currently in its science operations phase. Science data products are regularly produced over ocean for wind speed, surface roughness, and sensible and latent heat fluxes and over land for near surface volumetric soil moisture. Data products currently in development over ocean include tropical cyclone intensity (peak sustained winds) size (radius of maximum winds), extent (34, 50 and 64 knot wind radii), storm center location, and integrated kinetic energy. Over land, data products in development include refined versions of volumetric soil moisture content, flood inundation extent, time-varying inland water body maps, and riverine streamflow rate. An overview and the current status of the CYGNSS mission will be presented, together with updates on terrestrial science data products in development that are related to the terrestrial water cycle.

Fine particulate matter smaller than 2.5 micrometers (PM2.5) at the surface presents large health risks to the public. Providing long-term, spatially continuous data set of PM2.5 surface concentrations can support decision making by health departments, air agencies, as well as other stakeholders interested in public health. In this work, a website-based decision support tool will be shown. The tool includes two main parts. The first part is a multi-year, daily, 3-km database for PM2.5 concentrations in California from 2006 to present in both map and tabulate formats. The second part is a daily, 3-km, near real-time update of PM2.5 surface concentrations in California in the last seven days. The near real-time data are generated within 24 hours after the NASA A-train satellite swath. For both products, PM2.5 surface estimates are generated based on a fusion of EPA ground-based monitored data and satellite-derived PM2.5 data from the NASA Aqua/MODIS satellite sensor. This decision support tool can be used to provide quick and easy visualization of certain episodic events (such as California wildfires) on a daily basis. It can also be used to conduct multi-year statistical analysis, such as identifying the higher PM2.5 concentration days above 95th percentile as the days being impacted by wildfires. These datasets and the decision support tool is publicly available and can be accessed via the SJSU HAQAST website: http://www.met.sjsu.edu/weather/HAQAST/home.html

How and why has Geographic Information Systems (GIS) education been advancing in primary and secondary education, globally? How have GIS tools and approaches been spreading beyond geography and GIScience to earth and environmental science, business, data science, health, and other disciplines in higher education? The adoption and integration of GIS and related geospatial technologies into dozens of academic disciplines has led to worldwide high demand for instruction that is targeted and timely, a combination that is challenging to meet consistently with diverse audiences and in diverse settings. Over the past 40 years, methods and approaches for teaching and learning about geospatial technologies and with geospatial technologies have evolved in tight connection with the advances in the internet and personal computers. Academic degrees, concentrations, minors, certificates, and other programs abound within formal and informal education. During the educationally-disruptive COVID-19 period, online instruction has hastened the adoption of analysis-based and data-rich approaches such as GIS. What are the challenges that remain in the use of GIS in instruction and research, and how can these challenges be addressed? The author, who has been active in promoting and researching GIS in education at all levels for 30 years, will share his perspectives, research results, and offer a pathway forward.

The Salton basin is an arid, sub-sea level basin located in southeastern California. Mountain ranges border the basin directly to the west and east, to the north extends a narrow valley that terminates with a transverse mountain range, and to the south spans heavily irrigated, agricultural lands that gradually rise in surface elevation. Within the basin lies the Salton Sea, and surrounding the Sea is diverse terrain that includes dry playa, rocky and vegetated surfaces, and highly emissive alluvial fans and dry washes, among others. Although large dust outbreaks due to orographically forced high wind speed events are frequent in the area, measurements from a new AERONET site stationed within the basin shows that dust is a standard component of the region's atmosphere, even on days with otherwise low wind speeds. We find a diurnal cycle of background dustiness that peaks in the early afternoon. An analysis of boundary layer structure using a Vaisala CL51 suggests that deepening of the boundary layer due to dry convection results in downward mixing of momentum, increased surface wind speeds, and an accumulation of dust in the mixed layer. Further analysis of the CL51 observations at nighttime suggest that high AOD concentrations persist in the shallow nocturnal boundary layer, and can become elevated throughout the night due to westerly katabatic flows. An analysis of surface meteorological station data suggests possible pathways of suspended dust due to mountain and valley flows within the basin.

Lakes and wetlands are natural sources and have an important role for hydrological cycle. They are also affected by climate change and anthropogenic activities such as precipitation regime, diminishing water bodies, uncontrolled groundwater extraction, drainage and irrigation channels, agricultural activities and discharge of domestic and industrial waste. For this reasons monitoring the lake surface areas are very crucial. In this paper; the Seyfe Lake was selected as a case study. The study area is located at the southern part of the Seyfe basin with a catchment area of 1447 km2 in Central Anatolia. Seyfe Lake wetland is located in a closed basin. The lake area and its surroundings is a Ramsar site and have the status of Grade 1 Natural Site and Protected Natural Area. This study aimed to evaluate the spatio-temporal changes of the surface area of Seyfe Lake by using Geographical Information Systems (GIS) and remote sensing techniques. For this purpose Landsat (MSS, TM, ETM+, OLİ/TIRS) satellite images used for a period between 1987 and 2020. A total of 34 images were processed and, using unsupervised classification method, water body was extracted according to the spectral information of the image bands and the historical change of the lake surface area was calculated by using Image Analysis Function of ArcGIS 10.6.1 software. In addition to these satellite images, long-term hydrometeorological data and field observations were analyzed to determine the changes of the Seyfe Lake surface area and understand the effects of climate change and antropojenic activities. According to the old and recent satellite images of Seyfe Lake area, the persisting shrinkage of lake area and diminishing of the water resources over the years in the Seyfe Lake basin still continue. Maximum and minumum surface area of the lake was calculated 43 km2 and 18 km2 respectively during 1987-2020. In August 2008; the lake area is completely dried. Nowadays the 58% of the lake area is dried up. These variations could probably be related with the antropogenic effects and climatic changes in the study area. As a result, Satellite based techniques might provide a historical data to identify changes on the lake surface area. Remote sensing techniques are also effective to evaluate temporal and spatial changes on water resources resulted by human activities and local climate change. In-situ observation and hydrogeological monitoring are another important tools to support findings of remote sensing study. Keywords: Spatio-temporal change, Wetland, Seyfe Lake, Unsupervised Classification, Remote Sensing

Both high-power large aperture (HPLA) radars and smaller meteor radars readily observe the dense head plasma produced as a meteoroid ablates. However, determining the mass of such meteors based on the information returned by the radar is challenging. We present a new method for deriving meteor masses from single-frequency radar measurements, using a physics-based plasma model and finite-difference time-domain (FDTD) simulations. The head plasma model derived in~\citeA{dimopp17} depends on the meteoroids altitude, speed, and size. We use FDTD simulations of a radar pulse interacting with such head plasmas to determine the radar cross section (RCS) that a radar system would observe for a meteor with a given set of physical properties. By performing simulations over the observed parameter space, we construct tables relating meteor size, velocity, and altitude to RCS. We then use these tables to map a set of observations from the MAARSY radar (53.5 MHz) to fully-defined plasma distributions, from which masses are calculated. To validate these results, we repeat the analysis using observations of the same meteors by the EISCAT radar (929 MHz). The resulting masses are strongly linearly correlated; however, the masses derived from EISCAT measurements are on average 1.33 times larger than those derived from MAARSY measurements. Since this method does not require dual-frequency measurements for mass determination, only validation, it can be applied in the future to observations made by many single-frequency radar systems.

On 11 March 2020, the World Health Organization (WHO) declared Coronavirus disease (COVID-19) as a pandemic. The announcement had a cascading effect as countries around the world rushed to declare various states of emergencies. Canada was no exception. All Canadian provinces and territories implemented some health emergency measures to check the spread of COVID-19. This provided an opportunity to study the changes in seismic vibrations registered by the land-based seismic stations before, during, and after the lockdown. I analyzed continuous seismic data for 6 Canadian cities: Calgary (Alberta), Edmonton (Alberta), Montreal (Quebec), Ottawa (Ontario), Toronto (Ontario), and Yellowknife (Northwest Territories). These cities represent the wide geographical spread of Canada. The source of data for the study was seismic stations run by the Canadian National Seismograph Network (CNSN). Data available freely on the Incorporated Research Institutions for Seismology (IRIS) website was used. Python and ObsPy were used to load and convert raw data into Probabilistic Power Spectral Densities (PPSDs). The seismic vibrations in the PPSDs that fell between 0.1 HZ and 20 HZ were extracted and averaged for every two weeks period to determine the trend of seismic vibrations. The lockdown had an impact on seismic vibrations in almost all the cities I analyzed. Except for Ottawa, the seismic vibrations decreased between 14% - 44% with the biggest decrease in Yellowknife in the Northwest Territories. In the 3 densely packed cities of the population over 1 million - Toronto, Montreal, and Calgary, the seismic vibrations dropped by over 30%. In the case of Ottawa, the seismic vibrations increased by 8%. As not all seismic stations were equally close to the cities, they were not equally sensitive to changes in human activities. Furthermore, while lockdown happened in all the cities selected for the study, the strictness enforced and the participation of people in the lockdown varied. Many cities extended the lockdown without any change while others extended the lockdown with a loosening of restrictions. All these differences induced variations in the study. Finally, a comprehensive online training module was created using Jupyter notebooks to allow researchers to analyse lockdown data from other seismic stations.

The bulk of the sediment found on the abyssal plain is transported from the shelf to the deep ocean by marine turbidity currents. They consecutively erode, transfer and deposit large amounts of sediment determining the characteristics of the marine environment. Further, their destructive nature is evidenced by several failures of subsea infrastructures that have been associated with their passage. Many numerical models have been developed to study field-scale turbidity currents. However, these models do not include sediment transport processes typical of coastal settings. As such they currently require the prescription of boundary conditions usually difficult to assess. Here we present a new modeling approach to hindcast marine turbidity currents that is based on a three-dimensional application of the process-based model Delft3D capable of predicting the initiation of sediment gravity flows driven by wind- and wave-induced processes. Detailed numerical simulations were carried out to investigate the nature of a turbidity current that impacted upon a submarine pipeline offshore Philippines after the nearby landfall of a tropical cyclone. Our simulations predict the triggering of a severe turbidity current only after the passage of the aforementioned tropical cyclone and the absence of any significant undercurrent associated with the other most relevant cyclones that passed near the study area during the lifespan of the pipeline, matching field observations in the form of pipeline shifting. Numerical results describe the development of rip currents that flush out water and sediment in cross-shore direction, ultimately triggering a turbidity current into the submarine canyon that cross the pipeline where its displacement was detected. Collapse of similarity profiles of the predicted undercurrent with experimental measurements and field observations demonstrates the reliability of the model in capturing the vertical structure of turbidity currents. As Delft3D has been demonstrated to accurately reproduce sediment transport processes associated with different environmental pressures in various geomorphological settings, the proposed modelling approach will allow for a deeper understanding of the mechanisms involved in the initiation of marine turbidity currents and their potential forecast.