Worldwide coastal land-margins are prone to many flood hazards such as astronomical tides, tropical cyclones, sea-level rise, and extreme precipitation events. Compound flood events, in which two or more flooding mechanisms occur simultaneously or in close succession (Santiago-Collazo et al., 2019, https://doi.org/10.1016/j.envsoft. 2019.06.002), can exacerbate the inundation impacts due to the highly non-linear interaction of coastal and hydrologic processes. Furthermore, sea-level rise will increase the hazard at low-gradient coastal land-margins when assessing future projections due to its non-linear nuance on the compound flood (Santiago-Collazo et al., 2021, https://doi.org/10.3389/fclim.2021.684035). Therefore, there is an urgent need to develop new technologies capable of comprehensively studying compound flood events and identifying hotspots prone to these inundations. This research aims to develop a technique capable of defining and classifying coastal land- margins based on physically-based criteria due to surface flow hydrodynamics. A one-dimensional (1-D) hydrodynamic model was used to quantify the hydrodynamic response of thousands of different combinations of input parameters (e.g., astronomical tides, storm surge, precipitation, and landscape) that define a coastal land-margin. This 1-D fully-coupled model, based on the shallow water equations, was applied at a national spatial scale, considering several coastal watersheds within the Gulf of Mexico and the US East coast. One of the main goals of this tool is to identify coastal land-margins vulnerable to compound flood hazards over broad spatial scales (e.g., national or global scale). Findings suggest that low-gradient (e.g., slopes less than 0.01 m km-1) coastal land-margins are more susceptible to compound flood impacts than ones with a steeper gradient under most flooding scenarios. Future research will focus on applying this tool on a worldwide basis to test its capabilities at low-resolution, scarce data regions. A worldwide classification of coastal land-margins may help authorities, policy-makers, and professionals converge on better coastal resilience measures, such as comprehensive compound flood analysis to delineate accurate compound flood hazard maps.Full online poster version at agu2021fallmeeting-agu.ipostersessions.com/Default.aspx?s=FA-1F-20-67-21-4E-E7-69-9F-89-1E-33-BB-3D-2D-40

The Interstellar Boundary Explorer (IBEX) mission has shown that variations in the ENA flux from the outer heliosphere are associated with the solar cycle and longer-term variations in the solar wind. In particular, there is a good correlation between the dynamic pressure of the outbound solar wind and variations in the later-observed IBEX ENA flux. The time difference between observations of the outbound solar wind and the heliospheric ENAs with which they correlate ranges from approximately two to six years or more, depending on ENA energy and look direction. This time difference can be used as a means of “sounding” the heliosheath, that is, finding the average distance to the ENA source region in a particular direction. We apply this method to build a three-dimensional map of the heliosphere. We use IBEX ENA data collected over a complete solar cycle, from 2009 through 2019, corrected for survival probability to the inner heliosphere. We divide the data into 56 “macro-pixels” covering the entire sky, and as each point in the sky is sampled once every six months, this gives us a time series of 22 points per macro-pixel on which to time-correlate. Consistent with prior studies and heliospheric models, we find that the shortest distance to the heliopause dHP is slightly south of the nose direction (dHP ~ 110 – 120 au), with a flaring toward the flanks and poles (dHP ~ 160 – 180 au). The heliosphere extends at least ~350 au tailward, which is the distance limit of the technique.

Some of the Earth system data products such as those from NASA airborne and field investigations (a.k.a. campaigns), are highly heterogeneous and cross-disciplinary, making the data extremely challenging to manage. For example, airborne and field campaign measurements tend to be sporadic over a period of time, with large gaps. Data products generated are of various processing levels and utilized for a wide range of inter- and cross-disciplinary research and applications. Data and derived products have been historically stored in a variety of domain-specific standard (and some non-standard) formats and in various locations such as NASA Distributed Active Archive Centers (DAACs), NASA airborne science facilities, field archives, or even individual scientists’ computer hard drives. As a result, airborne and field campaign data products have often been managed and represented differently, making it onerous for data users to find, access, and utilize campaign data. Some difficulties in discovering and accessing the campaign data originate from the incomplete data product and contextual metadata that may contain details relevant to the campaign (e.g. campaign acronym and instrument deployment locations), but tend to lack other significant information needed to understand conditions surrounding the data. Such details can be burdensome to locate after the conclusion of a campaign. Utilizing consistent terminology, essential for improved discovery and reuse, is also challenging due to the variety of involved disciplines. To help address the aforementioned challenges faced by many repositories and data managers handling airborne and field data, this presentation will describe stewardship practices developed by the Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) under the NASA’s Earth Science Data systems (ESDS) Program.

Earthquake moment release is localized along a global fault system. This network of branching and anastomosing fractures defines the geometrically complex boundaries of tectonic plates and serves as the locus of contemporary elastic strain energy storage between earthquakes. The slow deformation of the earth’s crust in between earthquakes has been observed geodetically for decades and provides a filtered representation of the underlying earthquake behaviors. Here we describe efforts to model fault system activity at a global scale incorporating both tectonic plate motions and earthquake cycle effects. Interseismic earthquake cycle effects are represented using a first-order quasi-static elastic approximation, and these models yield a unified estimate of slip deficit rates and subduction zone coupling constrained by nominally interseismic geodetic surface velocity estimates. We present key findings from a kinematic global fault system model with 1.6×107 km2 of fault system area including 16 subduction zones and constrained by observations 22,500+ GPS velocities. Further, we describe new approaches to the efficient representation of viscoelastic deformation in large-scale block models and the prospects for high-resolution block scale models that directly image partial fault coupling across the entire global fault system. Because global geodetic observations capture faults behaviors at varying stages throughout the earthquake cycle, consideration of time-dependent deformation including viscous dissipation of coseismically induced stresses is important for accurate imaging of fault coupling. And, because concentrations of fault coupling have been shown to spatially correlate with recent significant earthquakes, being able to estimate partial coupling patterns on a global scale may highlight pending seismicity.

We study how ground frost affects the ambient seismic wavefield recorded by a three-component broadband sensor. By applying machine learning algorithms on continuous seismic data, we can retrieve the seismic signature of the continuous freeze and thaw process at the surface of the ground. The retrieved signature reveals that the presence of ground frost imprints the amplitude of the ambient seismic wavefield, and the energy ratio between horizontal and vertical components (H/V). A regression model can even predict diurnal freeze and thaw patterns based on the seismic data. Thus, we assume that slight changes in the physical properties of the frozen surface, such as the thickness, alter the seismic wavefield. Models of the subsurface with different properties of the ground frost agree with the observations from the field. The penetration depth of the ground frost, the temperature of the frozen ground, and the presence of different modes in the wavefield determine how the seismic wavefield is changing. The findings of this study show the potential of a single seismic station for monitoring frozen bodies near the surface, such as permafrosts.

Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. The accurate and rapid mapping of the flooded areas becomes more crucial when floods strike densely populated cities. During flood events near real-time satellite imagery has been proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water and permanent water. Binary segmentation using the threshold split-based method is commonly used in this regard, however, the generalization ability of this method is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated the excellent potential of Convolutional Neural Networks(CNN) for improving flood detection, although there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this project, we present a U-net based deep learning approach by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team and IEEE GRSS Earth Science Informatics Technical Committee. Dataset is composed of 66,810 tiles of 256×256 pixels, distributed respectively across the training, validation and test sets and cover flood events from Nebraska, North Alabama, Bangladesh, Red River North and Florence. Specifically we proposed an Unet architecture based convolutional neural network (CNN) with a backbone of EfficientNetb7, trained against the dataset. We then evaluated the performance of the model with multiple training, testing and validation. Two evaluation methods - Intersection over Union (IOU) and F-Score are adopted to evaluate the model performance. During testing, the model achieved the meanIOU score of 75.06% and F-Score of 74.98%. We hope to further improve the performance of the network by performing hyper-parameter tuning and to develop a model which can be used for near-real-time flood mapping.

Our objective is to test and improve cloud subcolumn generators used for greater realism of scales in the radiation schemes and satellite simulators GCMs. For this purpose, we use as guidance water content fields from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering significant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability which is what the generators are designed to produce. Our first order goal is to test the ability of the generators to deliver realistic 2D cloud extinction (cloud optical thickness) fields using, as in GCMs, limited domain-averaged information. Our reference 2D cloud extinction fields fully resolving horizontal (along the track of the satellites) and vertical variability come from combining CloudSat’s 2B-CWC-RVOD (liquid clouds) and CALIPSO-enhanced 2C-ICE (ice clouds) products. The combined fields were improved by introducing a simple scheme to fill liquid cloud extinction values identified as missing by comparing with coincident 2D (phase-specific) cloud masks provided by the CALIPSO-enhanced 2B-CLDCLASS-LIDAR CloudSat product. Our presentation will demonstrate the substantial improvements for low clouds brought by the filling scheme through comparisons with MODIS-Aqua cloud fraction distributions expressed in terms of joint cloud top pressure – cloud optical thickness histograms. Beyond global comparisons, the nature of the improvements become clearer when comparing mean joint histograms segregated by MODIS Cloud Regime (CR): improvement is by design superior for MODIS CRs dominated by low clouds. With the improved 2D extinction fields at hand, we test the skill of two subcolumn generators, one used in the COSP satellite simulator package, and one with more sophisticated cloud overlap implemented in the GEOS global model, to reproduce joint histograms that are statistically similar to the observed counterparts described above (as interpreted by COSP’s MODIS simulator). Our main comparison metrics are the Euclidean distance between observed and generator-produced global or near-global mean joint histograms, and the statistics of Euclidean distances calculated for individual scenes. One full year of data is used to assess whether the more sophisticated cloud generator produces clouds with greater realism in 2D cloud variability.

Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.