Dob’s Linn (Scotland) is a location that has significantly influenced our understanding of how life evolved over the Ordovician to early Silurian. The current chronostratigraphic boundary between the Ordovician and Silurian periods is a Global Boundary Stratotype Section and Point (GSSP) at Dob’s Linn calibrated to 443.8±1.5 Ma, partly based on biostratigraphic markers, radiometric ages, and statistical modeling. Graptolites are used here as relative dating markers. We dated hundreds of zircon grains extracted from defined metabentonites from six horizons exposed at Dob’s Linn using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). Each zircon was imaged using cathodoluminescence, and most show igneous zoning with minimal alteration. Sample locations range from 42 meters above to 5 meters below the recognized GSSP for the Ordovician-Silurian. Samples were responsibly collected and analyzed for paleontology and geochemistry in other work. Overall, many 238U-206Pb zircon ages from the section are significantly younger than expected. The youngest zircon in sample DL7, located 5 meters below the GSSP, yielded a 238U-206Pb age of 402±12 Ma (±2s, 5% disc). Nineteen spots on zircons from this sample are younger than the presently assigned GSSP age, including more concordant results of 426±8 Ma (0.8% disc) and 435±5 Ma (0.2% disc). The youngest zircon in sample 19DL12, < 1 m below the GSSP, is 377±8 Ma (2% disc) with a more concordant age of 443±7 Ma (0.6% disc). A sample located directly on the GSSP (19DL09) yields 327±5 Ma (0.8% disc). Eight spots on zircons from this sample are also younger than the presently assigned GSSP age. We also dated two samples (DL24 and BRS23) 8 meters above the GSSP, and the youngest, most concordant zircon ages in these samples are 400±11 Ma (5% disc) and 421±9 Ma (0.4% disc), respectively. Overall, the U-Pb ages would re-assign the Dob’s Linn chronostratigraphic section to Silurian-Devonian. The young age results could be attributed to Pb loss due to hydrothermal alteration during the Acadian and Alleghenian orogenies. Future work will implement Chemical Abrasion Isotope Dilution Thermal Ionization Mass Spectrometry (CA-ID-TIMS) to obtain accurate U-Pb dating and evaluate the potential effects of Pb loss.

Understanding onset of droughts and its potential linkage to resulting responses like severity (deficit volume) is crucial for providing timely information related to drought sectors including the cultivation planning and monitoring crop productivity. Using high-quality daily observed streamflow records from 82 medium-to-large sized catchments over (tropical) peninsular India, we show that the variability in onset timing drives the severity of hydrological droughts. The strength of onset timing-severity relationships using observed records indicate seasonality of rainfall and catchment characteristics mainly modulate hydrological drought responses in peninsular India, which is not readily apparent from land-surface model simulations. The observed trend for mean onset of drought depicts delayed occurrence for more than half of the catchments. Around one-third of the catchments shows a stronger non-linear significant dependency (>0.7) between severity and onset of drought. The findings of the study highlight the need for accounting feedback between drought onset and severity and their concurrent changes for seasonal-to-sub-seasonal predictability of droughts; and contributes to discussions on building resilience to extreme droughts in a changing climate.

More than half of Earth is covered by ocean, yet little is known about the deep sea (> 200 meters), particularly the water column. From 200 to 1,000 meters below the ocean surface is the mesopelagic zone, where a variety of organisms thrive. Within this zone, there is a portion of the water column where mesopelagic organisms are highly abundant due to light differences and predator avoidance, causing the region to be acoustically dense; this is known as the deep scattering layer (DSL). We hypothesized that there are more organism sightings per minute in the DSL than other regions of the water column and more organism sightings per minute in the Gulf Stream than outside the Gulf Stream. NOAA Ocean Exploration conducted video transects in the mesopelagic zone during expeditions in the deep waters off the Southeastern United States and Puerto Rico using NOAA Ship Okeanos Explorer and remotely operated vehicles (ROVs), Deep Discoverer and Seirios. During three expeditions from 2018 and 2019, imagery, environmental, and acoustic data were collected. Satellite-derived current data were also incorporated for Gulf Stream comparisons. The number of sightings per minute above, within, and below the DSL differed significantly. There were significantly more sightings per minute inside than outside the Gulf Stream. This furthers our understanding of the organisms present in these regions, which is important because of the movement of nutrients in the Gulf Stream and DSL organisms’ role in the food web.

The modeling of physical phenomena oftentimes leads to partial differential equations (PDEs) that are usually nonlinear and can also be subject to various uncertainties. Solutions of such equations typically involve multiple spatial and temporal scales, which can be numerically expensive to fully resolve. On the other hand, for many applications, it is large-scale features of the solutions that are of primary interest. The closure problem of a given PDE system seeks essentially for a smaller system that governs to a certain degree the evolution of such large-scale features, in which the small-scale effects are modeled through various parameterization schemes. 
 We will present an approach to parameterize the unresolved small-scale dynamics using the resolved large scales for forced dissipative systems. We will show that efficient parameterizations can be explicitly determined as parametric deformations of geometric objects constructed from dynamically based analytical formulas. The minimizers are intimately tied to the conditional expectation of the original system. We will highlight, within a variational framework, a simple semi-analytic approach to determine such parameterizations based on backward-forward auxiliary systems and short solution data. Concrete examples arising from geophysical considerations will also be presented to illustrate the effectiveness of the approach.

Despite human-induced changes in floodplains over the past century, comprehensive data of long-term land use change within floodplains of large river basins are limited. Data of long-term and large-scale floodplain land use are required to effectively quantify floodplain functions and development trajectories. They also provide a holistic perspective on the future of floodplain management and restoration – and concomitantly flood-risk mitigation. Here, we present the first available dataset that provides spatially explicit estimates of land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at a 250-m resolution. We derived this MRB floodplain land use change dataset from two input data sources: (i) the high-resolution global floodplain extent dataset GFPLAIN250m, and (ii) the annual FOREcasting SCEnarios of Land-use Change (FORE-SCE) dataset for the continental United States. Our results suggest that MRB floodplains have transitioned irreversibly from natural ecosystems to predominantly agricultural land use (e.g., more than 10,000 km2 of wetlands have been lost due to agricultural expansion). Developed land use within the floodplain has also steadily increased. The dataset is publicly available through HydroShare: https://gishub.org/mrb-data as well as an interactive online map interface: https://gishub.org/mrb-floodplain. These products will support MRB resilience and sustainability goals by advancing data-driven decision making on floodplain restoration, buyout, and conservation scenarios.

The increasing availability of surface water inundation data has encouraged modelers and managers to include small yet abundant surface water storage systems (e.g., wetlands and other landscape depressions) in process-based models. Yet, these model applications have been largely limited to small- to meso- watershed scales, with drainage areas ranging from a few hectares to several thousand square kilometers. The conventional practice of overlooking these surface water storage systems in basin-scale (e.g., >10,000 m2) hydrologic modeling may be missing the total picture of flood and drought hazards. To fill this gap, we developed a 30-m resolution topography-based wetland and depression storage (maximum surface area and storage volume) database for the Upper Mississippi, Ohio, and Missouri River Basins ⎼ encompassing the 2.35 million km2 upstream domain of the Mississippi River system. Further, we integrated this depression dataset into a process-based model to simulate sub-catchment and river reach-scale hydrologic fluxes (surface runoff, soil wetness, evapotranspiration) and flows (streamflow). Compared with a “no depression” conventional model constructed for the Missouri and Upper Mississippi River Basins, our exploratory analyses demonstrate that a depression-integrated model (i) significantly alters the spatial patterns and magnitudes of water yields, (ii) improves streamflow simulation accuracy, and (iii) provides realistic spatial distributions of landscape wetness conditions. These emerging findings provide us with new insights into the effects of small surface water storage and stimulates a reassessment of current practices for basin-scale hydrologic modeling and water management.

Introduction HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided Objectives The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated. Presentation The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.

Introduction HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided Objectives The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated. Presentation The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set focusing on performance at the coast. It will also present the results of a study assessing regional tidal models.

Bedforms (ripples, dunes, sandwaves) are ubiquitous features in many sandy subaqueous settings. They have been observed in a wide variety of flows, including rivers, the surf zone, estuaries, tidal inlets, shallow seas, and deep waters. Bedforms exert a major influence on a range of processes, from small-scale turbulence and sediment transport to large-scale coastal geomorphology. Therefore, knowledge on the dimensions, morphological characteristics and dynamics of large bedforms is relevant for a range of fundamental and applied research. Several methods have been developed over the years to characterise bedform dimensions from bathymetric data. Each method has been created for a specific purpose (e.g. discriminate bedform scale, calculate bedform size and/or shape, detect crestlines) and environment (unidirectional, constrained tidal or open marine) and with a certain accuracy (precise time-consuming detection or coarse rapid detection). Although some of these methods are freely available, it may be difficult for scientists to use them due to the specificity of their design. A unique toolbox which combines the available methods into one easy-to-use software would help the bedform community advance knowledge on bedform research by facilitating the analysis of bedform characteristics. This should also include recommendations of which method should be used for which purpose. The present project aims at creating a Bedform Analysis Toolbox which combines several methods already available. The toolbox will be made open source and freely available. Feedback on the need of the community or required design and specificity would help us create a toolbox which is useful to many scientists.

Subaqueous dunes are fascinating morphological features that exist in diverse environments such as the deep sea, continental shelves and inland streams or rivers. Due to their rhythmic and oftentimes very frequent occurrence along the predominant flow direction, the analysis of these bedforms is usually assigned to bedform identification tools. Such algorithms automatically determine crest and trough locations and calculate dune dimensions accordingly. Over the last years, the number of these tools has notably increased with specialized methodologies for every environment and bedform scale. Although many of them are readily available to interested researchers, there may be uncertainty as to which method should be applied in view of a specific research question. As authors of some of the most recent bedform identification tools, we have started to systematically compare our approaches by analyzing an agreed set of diverse bathymetric data. The bed features assessed in this context range from river dunes formed under unidirectional flow over tidally constrained compound dunes to bed elevation profiles gained from flume experiments. The resulting dune characteristics, which each scientist obtained by applying his/her particular algorithm, are thereupon contrasted in a qualitative and quantitative manner uncovering the similarities and differences between individual methodologies. Our preliminary results suggest a strong influence of the original focus of each algorithm and therefore corroborate the need for systematic comparison. In the next step, the gained insights will be used to find and explain the optimum fields of application and, in the end, provide user-oriented guidelines that may support the bedform community in deciding which identification tool should be used for what purpose.

Researchers dealing with flood hazard and risk assessment typically refer to available in-situ gauging stations for the calibration and validation of hydrological and hydrodynamic (HD) models. However, lack of dense gauging data such as stage-discharge relationship (rating curves) results in higher uncertainty in hydrological studies. The multi-mission satellite altimetry observations are capable since long time to monitor continental water bodies at regular interval. In this study, combined use of hydrodynamic model and satellite altimetry data has been exploited for establishing virtual gauging network in flood-prone sparsely gauged river basin. The virtual gauging stations were established at locations where ground tracks of various altimeters (Jason-2/3, SARAL/AltiKa, and Sentinel-3A/3B) cross the river channel. Rating curves were generated at these virtual stations using calibrated and validated HD model. High agreement between the simulated and altimetry-based water levels at virtual stations showed the potential of satellite altimetry data for the multi-site validation of HD model and constructed rating curves. The availability of water level time series and rating curves at multiple virtual locations in addition to existing in-situ physical gauging stations provides an opportunity to expand gauging network in sparsely gauged basin. Thus, the proposed framework may open new perspectives for enhancing river flow dynamic studies considering upcoming satellite missions like SWOT which will ensure more observations in future.

In the two years since the Fall 2019 meeting, NOAA/NESDIS completed two significant projects whose goal was to prototype an enterprise data management system in the cloud. The two pilot efforts, run by the NESDIS Cloud Integrated Project Team, were completed in March of 2020 and March of 2021. A significant portion of those pilot efforts has been operationalized into what is known as the NESDIS Common Cloud Framework (NCCF). The NCCF is now up and running and capable of securely ingesting data and generating operational products. Another effort, the NESDIS Cloud Archive Project, picked up the unfinished work of the NESDIS cloud pilots and is focused on delivering an end-to-end cloud archive prototype in late 2021, with the goal of operationalizing that cloud archive capability into the NCCF in 2022. The current status of these efforts will be presented, including a technical focus on how NESDIS plans to archive data using commercial cloud services.

The increasing amount of Synthetic Aperture Radar (SAR) satellites orbiting the Earth, their increasing time and space resolution, the variety of wavelengths, polarizations and looking geometries, the shortening of data availability latency and the lengthening of archive databases offer unprecedented opportunities for Earth observation and hazard monitoring. The downside is that it brings new challenges for processing that huge amount of data and for making the results quickly analyzable. To fully benefit from these advances in SAR, it requires efficient data processing infrastructure (optimized for processing speed, storage usage and security), efficient data visualization, and standardization of the final products for easy ingestion by conventional analysis tools. We present here the InSAR Mass processing Toolbox for Multidimensional time series (MasTer), which can combine any type of SAR data to produce unsupervised vertical and horizontal ground deformation time series. MasTer is optimized to automatically download SAR data, select the appropriate interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is made available. The incremental architecture allows updating the time series within the shortest time possible (typically a few hours) as soon as a new SAR image is provided. Several steps are self-evaluating to ensure robust and reliable processing. Moreover, recent methodological improvement consists in the computation of a coherence proxy to guide the pair selection optimization balancing the use of each image as primary and secondary image during the differential interferometric (DInSAR) processing. Such a pair selection increases both the processing efficiency and the signal-to-noise ratio of the time series. MasTer also allows the production of time series of coherences or SAR amplitude images, which can be used e.g. for land use monitoring or geomorphological changes detection. The capabilities and performances of MasTer will be illustrated with several examples. Software and manual are available upon request from the authors.

Deep earthquakes in the lower continental lithosphere - the lower crust and uppermost mantle - are frequently too poorly located in depth to be definitively labelled as having occurred above or below the Mohorovičić discontinuity (Moho; base of the crust). Our Sn/Lg methodology utilizes two regional seismic waves to determine the depth of an earthquake relative to the Moho: Sn and Lg waves, which are phases that propagate through reflections in the mantle lid and the crust, respectively. Therefore, an analysis of Sn and Lg waves can provide a robust understanding of an earthquake’s depth relative to the Moho. We present our Sn/Lg analysis through reduced-velocity record sections, which show Sn and Lg energy in the waveform, allowing measurements of RMS amplitudes, and maps of ray-paths and Sn/Lg amplitude ratios which allow us to visualize the propagation of Sn and Lg in all directions. We demonstrate the efficacy of our approach by applying it to a well-known upper-mantle earthquake in Wyoming and a shallow earthquake with a similar epicenter. We then use our method to study other deep-crustal/upper-mantle earthquakes in North America. A cluster of earthquakes with reported depths from 10–50 km ± ~10 km, spans the border between Alberta, Canada and Montana, U.S.A. where the crustal thickness increases from ~30 km in the SW to ~45km in the NE. For an earthquake occurring in the crust-mantle transition zone, the dipping Moho should tend to block Sn (decrease the Sn/Lg ratio) in the direction of crustal thickening, and tend to block Lg (increase the Sn/Lg ratio) in the direction of crustal thinning. We studied seven earthquakes of magnitude>2.5. A m=2.7 earthquake, previously reported (USGS PDE) to be at 50±10 km where CRUST 1.0 shows a 49 km Moho depth, and a m=3.5 reported at 38 km depth above a nominal 43 km Moho, both show much stronger Sn/Lg ratios than earthquakes with nearby epicenters at nominal dots of 15 and 21 km. Hence the “50-km” and the “38-km” earthquakes must occur in the upper mantle, or so close to the upper mantle as to preferentially excite Sn. The better-recorded of these earthquakes also shows clear evidence of Sn enhancement along azimuths into regions of thinner crust.

Data-model comparisons are common when addressing (paleo)climate questions. Many applications require deriving continuous surface fields of scalar variables from a set of irregularly distributed data points, typically for model validation against data or data-derived model input as initial or boundary conditions. While various interpolation techniques and interfaces exist, few can simultaneously: (1) interpolate across local to global spatial scales, (2) perform anisotropic interpolation using the spatial structure derived from the data instead of an assumed one, and (3) explicitly derive uncertainty in the interpolated fields due to both data density and measurement error. We present a standalone interpolation toolbox including a graphical user interface (GUI), which is aimed at the general earth science community. It uses a kriging algorithm whose distance metric is the geodesic on an oblate spheroid, be it the WGS-84 reference ellipsoid for applications on the surface of the Earth, or an equivalent ellipsoid with varying radii for interpolation on vertical levels above the surface. While kriging algorithms exist that perform interpolation on such non-Euclidean distances, they do not provide a check for conditionally negative semi-definiteness (CNSD) of the variogram matrix, which is a requisite for the kriging method. Since mathematical theory of kriging on spheroids or ellipsoids has not yet provided a set of authorized variance-distance functions, we incorporated a numerical check for CNSD condition for each data realization and variance-distance modeling scheme. The GUI will allow the user a high degree of customization. Preliminary results are promising, with robust results for isotropic interpolation. The derivation of CNSD variogram matrices for anisotropic interpolation remains the major challenge of the project. When completed, ClimAG-Krigger will provide the community with an easy-to-use, robust tool for anisotropic global kriging that will be specifically tailored for (paleo)climate applications.

Earthquake signals in seismic data are inevitably contaminated with signals from unwanted sources. Separating noise from earthquake signals can greatly improve the analysis of the seismic data, such as earthquake characterization and ambient noise analysis. In this work, we develop a new auto-encoder to extract transient signals from ambient signals directly in the time domain for 3-component seismograms. We benchmark our architecture development and performance against a time-frequency counterpart (similar to the DeepDenoisier). We explore the generalization of our time-domain denoiser by training on various scales of seismic data. First, we train purely on observed seismograms of local ( < 350 km) events using the STandford EArthquake Dataset (STEAD) data set. Second, we generate a data set of observed seismograms from regional earthquakes (350 km-2000 km), which we complement with seismograms generated by hybrid low-frequency deterministic, high-frequency stochastic synthetic waveforms. We explore the robustness of the denoiser on various noise structures. Finally, we explore the quality of the extracted signals, for earthquake characterization and for ambient noise seismology.

Southeastern Greenland (SEG) provides a complex habitat area consisting of dozens of deep fjords that connect land-based ice with the open ocean. Within these fjords, glacial ice mixes with sea ice and intricate topography can create niche local conditions. This area is important for a number of species, including polar bears, and better understanding of both land ice and floating glacial ice as biological habitat motivates the need to study the physical environment itself. Studying SEG, however, posed a variety of challenges, including difficult access for in-situ work or instrument deployment, cloudy conditions that can obscure optical satellite instruments, and steep, complex terrain that can complicate identification of surface conditions. Here, we discuss our work to leverage several remote sensing products to determine the geospatial patterns of landfast sea ice and solid glacial ice during 2015-2019 in five SEG fjords with high polar bear use. We further connect these data with measurements of solid glacial ice discharge and glacial and terrestrial freshwater flux across SEG. The landfast sea ice season in our focus fjords is quite short, extending on average only ~2-4 months, and including substantial variability. Because of the fjord connections to marine-terminating glaciers, however, solid glacial ice creates a potential alternative ice platform on the fjord surfaces that can complement the short sea ice season. Challenges remain in automating this type of surface classification, and we discuss how our manual digitization work can be leveraged to support other ongoing efforts to create machine learning surface identification algorithms.

Previous approaches to modify the surface albedo of Arctic ice have been proposed to slow the rate of ice melt or to increase ice thickness. (Seitz 2011, Desch 2018, Field 2018) Sea ice, glacial ice, and ice-covered thermokarst lakes are all known to melt aided by surface albedo feedback loops. In this process, melting of snow and/or the formation of melt ponds on the surface of the ice causes a lowering of surface albedo, which causes more absorption of sunlight, further warming the ice and increasing the rate of melt. In this study on a freshwater pond, we demonstrate the real-time dynamics of melting and a reduction of ice melt rate by 26% via surface albedo modification.