We aim to better understand the spatial distribution of interseismic overriding plate deformation at and near subduction zones. To this end, we analyze horizontal GNSS velocities in South America, southeast Asia, and northern Japan, computing and interpolating local trench-normal and -parallel velocity components. Velocities generally decrease with distance from the trench with a steep gradient up to a “hurdle”, beyond which the gradient is distinctly lower and velocities are near-zero. The hurdle is located 500–1000 km away from the trench for the trench-perpendicular component and either at the same distance or closer for the trench-parallel. In contrast, significant displacements during large megathrust earthquakes are generally observed beyond the hurdle. To test our hypothesis that the hurdle results from a lateral contrast in overriding plate compliance, we use cyclic three-dimensional finite element models . Our results are consistent with the observed interseismic velocity gradients and far-field coseismic displacement. The gradient in modeled trench-perpendicular velocities depends on the location of the contrast and on the plate compliance on both sides. Trench-parallel velocities have a progressively shallower gradient with distance from the trench and only depend on the near-trench modulus. The inferred contrast probably results from thermal, compositional and thickness contrasts. This interpretation is consistent with the presence, close to the observed hurdle, of major tectonic or geological boundaries separating the plate margin from a distinct, and likely less compliant, plate interior. Stress accumulation on the model’s locked megathrust patches is hardly affected by the distance to the contrast.

Hillslopes are responsible for the production and transport of sediments within a landscape (Gilbert 1877). Since the hillslope gradient and morphology tend to vary across a landscape, it is expected that the erosion and sediment delivery would also be non-uniform. In this study, we explore the probability of the flux at a particular point in the catchment reaching the river mouth using connectivity and the Revised Universal Soil Loss Equation (RUSLE) in the Pranmati river catchment (a small 3rd order Himalayan river catchment within the Ganga River system). Methodology involves characterising the hillslopes of Pranmati river catchment centered on land use and land cover units. Using RUSLE, the sediment yielding capacity of various land cover units are estimated based on which potential source areas are marked. The sediment connectivity within the basin is also calculated by generating a sediment connectivity map of the area using method given by Borcelli et al. (2008). The catchment is categorized into four classes – (A) Highly connected zones with high sediment yielding capacity (B) highly connected zones but low yielding capacity (C) poorly connected zones but high yielding capacity (D) poorly connected zones and low yielding capacity. The area is then mapped on the basis of the defined classes and potential areas of erosion and storage are identified. Our results show that about 62% of the catchment area has low connectivity implying sediment flux generated in these zones have a low probability of leaving the catchment. Only 11% of the catchment area has sediment yield greater than the mean yield per hectare. The sediment generated from this small area of the catchment contributes 93% of the total sediment production of the catchment. References Borselli, L., Cassi, P., & Torri, D. (2008). Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment. Catena, 75(3), 268-277. Gilbert, G. K. (1877). Geology of the Henry mountains (pp. i-160). Government Printing Office.

Enhanced water management systems depend on accurate estimation of hydraulic properties of subsurface formations. This is while hydraulic conductivity of geologic formations could vary significantly. Herein, we studied an intensively managed area located in the Upper Sangamon Watershed in Central Illinois, U.S.A., and generated 2D maps of hydraulic conductivity over a large-scale region with quantified uncertainties in different depth layers. In doing so, we made use of low cost, small-scale measurements obtained from the Electrical Earth Resistivity together with more accurate, more expensive pumping tests in a calibration framework based on Kriging. We offered a cost-effective approach to reliably characterize the hydraulic conductivity properties in under-sampled sites and can be particularly used in obtaining large-scale parameter maps for a region using small-scale measurements in an efficient way. This work also includes optimal sensor placement, where the best locations for future data collection are selected by considering the current confidence levels estimated by the Kriging model, which is related to the expected value of information from future sensor data. Our approach is based on the Bayesian experimental design, which selects the best locations, out of a set of candidate locations, based on the value of information that each location is expected to offer.

Turbulence generated by aquatic vegetation plays a vital role in the interfacial transfer process at the air-water interface and sediment-water interface (AWI and SWI), impacting the dissolved oxygen (DO) level, a key indicator of water quality for aquatic ecosystems. We investigated the influence of vegetation, under different submergence ratios and plant densities, on the interfacial gas transfer mechanisms. We conducted laboratory experiments in a unidirectional recirculating flume with simulated rigid vegetation on a sediment bed. Two-dimensional planar Particle Image Velocimetry (2D-PIV) was used to characterize the mean flow field and turbulent quantities. Gas transfer rates at the AWI were determined by monitoring the DO concentration during the re-aeration process in water. SWI interfacial transfer fluxes were estimated by measuring the DO concentration difference between the near-surface and near-bed values. Compared to previous observations on a smooth bed without sediment, the presence of sediment enhances the bottom roughness, which generates stronger bed-shear turbulence. The experimental result shows that turbulence generated from the bed does not affect the surface transfer process directly. However, the near-bed suspended sediment provides a negative buoyancy term that reduces the transfer efficiency according to the predictions by a modified Surface Renewal model for vegetated flows. The measured interfacial transfer fluxes across the SWI show a clear dependence on the within-canopy flow velocity, indicating that bed shear turbulence and within-canopy turbulence are critical indicators of transfer efficiency at SWI in vegetated flows. A new Reynolds number dependence model using near-bed turbulent kinetic energy as an indicator is proposed to provide a universal prediction for the interfacial flux across the SWI in flows with aquatic vegetation. Our study provides critical insight for future studies on water quality management and ecosystem restoration in natural water environments such as lakes, rivers, and wetlands.

Stream water temperature is considered a “master variable” in environmental processes and human activities. Existing process-based models have difficulties with defining true equation parameters, and sometimes simplifications like assuming constant values influence the accuracy of results. Machine learning models are a highly successful tool for simulating stream temperature, but it is challenging to learn about processes and dynamics from their success. Here we integrate process-based modeling (SNTEMP model) and machine learning by building on a recently developed framework for parameter learning. With this framework, we used a deep neural network to map raw information (like catchment attributes and meteorological forcings) to parameters, and then inspected and fed the results into SNTEMP equations which we implemented in a deep learning platform. We trained the deep neural network across many basins in the conterminous United States in order to maximize the capturing of physical relationships and avoid overfitting. The presented framework has the ability of providing dynamic parameters based on the response of basins to meteorological conditions. The goal of this framework is to minimize the differences between stream temperature observations and SNTEMP outputs in the new platform. Parameter learning allows us to learn model parameters on large scales, providing benefits in efficiency, performance, and generalizability through applying global constraints. This method has also been shown to provide more physically-sensible parameters due to applying a global constraint. This model improves our understanding of how to parameterize the physical processes related to water temperature.

As the sea level rises, it is alarming that the threat from flooding induced by tropical cyclones would cause more severe damages to coastal regions worldwide. In order to address this threat, optimizing coastal protective or mitigation strategies is necessary, given limited resources. The optimization methodology must incorporate feedback from stakeholders for practical use. Multiple interviews were conducted by engineering model developers and social scientists with stakeholders who have first-hand knowledge and varied backgrounds in New York. The protective strategies have been tuned to the critical infrastructure's particular and contextual risks due to flood hazards by engaging and integrating stakeholders' knowledge on the interdependency of the infrastructures and other aspects after the first interview. The second interview was conducted for further model improvement.

Coastal regions are continuously under the threat of flooding induced by tropical cyclones worldwide. These threats continue to increase due to the effects of climate change such as sea-level rise. A number of available protective or mitigation strategies have been examined to address this threat and protect coastlines around the world. However, identifying the most effective strategy given limited resources is a complex question. Optimization methodologies as we have proposed integrate physical analysis and stakeholder feedback to come to a set of best mitigation strategies. This study examines physical and socio-economical aspects of flooding impacts to optimize these strategies. These are then examined including seawalls, elevated promenades, and strategic retreats.

Knowing the heterogeneous crustal structure is essential for understanding the ice dynamics, glacial isostatic adjustment (GIA) and tectonic history in Antarctica. For example, geothermal heat flux (GHF) is a major boundary condition for ice dynamics and the crust thickness and its composition (mafic or felsic) are important factors in GHF. Meanwhile, the GIA signal and its gravity response are essential for detecting mass-balance change and predicting future sea-level change. Errors in the density model used, which may be over 10%, will propagate into the gravity calculations. In this study, we use gravity inversion constrained by seismic depth estimation to recover the heterogeneous crustal structure of Antarctica, and estimate its uncertainties. Specifically, we modify by inversion the density of the uppermost mantle, the crustal density, the Moho depth, and the sedimentary cover thickness with an ensemble model with different density/geometry variation constraints. The output models indicate the most representative model of Antarctic crustal structure within the capacity of the method and current data constraints. Our preliminary results show that crustal density varies between 2.75 to 2.95 g/cm3 while the Moho depth varies between 22 km in Ross Ice Shelf and 54 km in Gamburtsev Subglacial Mountains. Low-density sedimentary basins are modelled at up to 10 km thickness beneath the ice shelf, and 3 km inland of Antarctica. Model also shows mantle density varies from 3.25 to 3.35 g/cm3. These density and thickness variations indicate likely substantial differences in crustal heat production, crustal rheology, and the expected GIA response of Antarctica’s crust.

The UN identified water as a human right in 2010; however, the bodies analyzing and designing water management often do not represent a diverse spectrum of the populations they serve. Villanova Center for Resilient Water Systems (VCRWS) research focuses on supplying resilient and sustainable solutions to global water and stormwater challenges. A diverse team of faculty, staff and students created a group that discusses diversity, equity, and inclusion (DEI) in the context of water resources engineering and associated design and solution considerations. The group has discussed a wide array of topics through the biweekly meetings. Some benefits of this group included the opportunity for personal growth and development, education on incorporating DEI into engineering thought, and re-education on DEI issues. This group served as a mechanism for members to share their grief and concerns over the political, social, and environmental issues that impede positive change. Outputs from this group include (1) competing in the Schiller Challenge through writing a paper on DEI in water engineering as a group, (2) drawing an action plan to further incorporate DEI in the activities of VCRWS through research, symposiums, conferences, public outreach programs, articles and papers, and engineering education, and (3) creating a guideline for creating DEI groups in other departments, research centers and institutions. This presentation will discuss lessons learned from creating and implementing a successful DEI focused working group within the water resource research center and will include some of the future work planned by the VCRWS group, such as incorporating DEI into the Water and Environmental Engineering curriculum through curating audiovisual materials on DEI concepts, and case studies of successes stories of DEI.

Rain gardens are green stormwater infrastructure that are designed to leverage natural processes to mitigate the impacts of urban stormwater through capturing, infiltrating, and filtering run off. Overtime these systems have the potential to buildup fines and nutrients, impacting their sustainable function. A rain garden’s performance depends on its ability to infiltrate runoff which can be reduced by clogging. Another concern is the potential transport of contaminants from rain gardens to groundwater through deep drainage. This study analyses the spatial and temporal distribution of fines and nutrients in three rain gardens through comprehensive field tests, laboratory testing, and computation analysis. Geomorphic studies were performed by integrating the digital elevation models, derived from Lidar surveys, with the FastMech solver within International River Interface Cooperative (iRIC) software, to model shear stress distribution and sediment transport relative to spatial observations of soil texture and nutrient concentrations within the rain garden. The soil properties were also used in creating models of water infiltration and nutrient sorption using Hydrus 1D. Results show that shear stresses in localized sections of each rain garden can be correlated with fines and nutrient distributions, allowing for prioritizing locations for maintenance. To conclude, LiDAR scans, flow and shear stress models, infiltration and nutrient transport models, field and laboratory soil tests can help us understand the surface dynamics and soil attributes, and gradually gain insight into the GSI performance with time.

The MHD with embedded PIC (MHD-EPIC) model makes it feasible to incorporate kinetic physics into a global simulation. Still, this requires a large enough box-shaped PIC domain to accommodate the movement and changes of the magnetic reconnection regions over time. This wastes computational resources on simulating regions with the expensive PIC model where MHD would be sufficient to describe the physics. We have developed a new MHD with Adaptively Embedded PIC (MHD-AEPIC) algorithm that couples the BATS-R-US MHD model with the new FLexible Exascale Kinetic Simulator (FLEKS) PIC code. In the new coupled model the PIC domains can move with the magnetic reconnection regions and adapt to them with an arbitrary shape. In this work, we will first introduce the algorithms for selecting the reconnection regions in the MHD model that need to be resolved with the kinetic PIC model. Then we will compare simulations obtained with MHD-EPIC using fixed PIC regions versus MHD-AEPIC employing adaptive PIC regions to verify that the new model generates reliable results. Finally, we will apply the MHD-AEPIC model to a global magnetic storm simulation and demonstrate the improved efficiency.

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.