Amongst the numerous existing methods to characterize fractured media, previous work has demonstrated that geoelectrical measurements, acquired either along the Earth's surface or in boreholes, may provide important information regarding fracture network properties. However, the lack of numerical approaches adapted to the strong contrast in geometrical and electrical properties between the fractures and the rock matrix prevents us from systematically exploring the links between geoelectrical measurements and fractured rock properties. To address this issue, we present a highly computationally efficient methodology for the numerical simulation of geoelectrical data in 2.5-D complex fractured domains. Our approach is based upon a discrete-dual-porosity formulation, whereby the fractures and rock matrix are treated separately and coupled through the exchange of electric current between them. Our methodology is validated against standard analytical and finite-element solutions and used to simulate geoelectrical data for a variety of different fracture configurations. This results in demonstrating the sensitivity of these data to important parameters such as the fracture density, depth, and orientation, and in opening new perspectives in terms of the inversion of geoelectrical data in order to characterize fractured rocks.

Baker et al (2015) developed the Community Earth System Model Ensemble Consistency Test (CESM-ECT) to provide a metric for software quality assurance by determining statistical consistency between an ensemble of CESM outputs and new test runs. The test has proved useful for detecting statistical difference caused by compiler bugs and errors in physical modules. However, detection is only the necessary first step in finding the causes of statistical difference. The CESM is a vastly complex model comprised of millions of lines of code which is developed and maintained by a large community of software engineers and scientists. Any root cause analysis is correspondingly challenging. We propose a new capability for CESM-ECT: identifying the sections of code that cause statistical distinguishability. The first step is to discover CESM variables that cause CESM-ECT to classify new runs as statistically distinct, which we achieve via Randomized Logistic Regression. Next we use a tool developed to identify CESM components that define or compute the variables found in the first step. Finally, we employ the application Kernel GENerator (KGEN) created in Kim et al (2016) to detect fine-grained floating point differences. We demonstrate an example of the procedure and advance a plan to automate this process in our future work.

Since many years precession is regarded as an alternative flow driving mechanism that may account, e.g., for remarkable features of the ancient lunar magnetic field [Dwyer 2011; Noir 2013; Weiss 2014] or as a complementary power source for the geodynamo [Malkus 1968; Vanyo 1991]. Precessional forcing is also of great interest from the experimental point of view because it represents a natural forcing mechanism that allows an efficient driving of conducting fluid flows on the laboratory scale without making use of propellers or pumps. Within the project DRESDYN (DREsden Sodium facility for DYNamo and thermohydraulic studies) a dynamo experiment is under development at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in which a precession driven flow of liquid sodium with a magnetic Reynolds number of up to Rm=700 will be used to drive dynamo action. Our present study addresses preparative numerical simulations and flow measurements at a small model experiment running with water. The resulting flow pattern and amplitude provide the essential ingredients for kinematic dynamo models that are used to estimate whether the particular flow is able to drive a dynamo. In the strongly non-linear regime the flow essentially consists of standing inertial waves. Most remarkable feature is the occurrence of a resonant-like axisymmetric mode which emerges around a precession ratio of Ωp/Ωc = 0.1 on top of the directly forced re-circulation flow. The combination of this axisymmetric mode and the forced m=1 Kelvin mode is indeed capable of driving a dynamo at a critical magnetic Reynolds number of Rmc=430 which is well within the range achievable in the experiment. However, the occurrence of the axisymmetric mode slightly depends on the absolute rotation rate of the cylinder and future experiments are required to indicate whether it persists at the extremely large Re that will be obtained in the large scale sodium experiment.

Accurately estimating the net seasonal snow accumulation (or “winter balance”) on glaciers is central to assessing glacier health and predicting glacier runoff. However, measuring and modeling snow distribution is inherently difficult in mountainous terrain, resulting in high uncertainties in estimates of winter balance. Our work focuses on uncertainty attribution within the process of converting direct measurements of snow depth and density to estimates of winter balance. We collected more than 9000 direct measurements of snow depth across three glaciers in the St. Elias Mountains, Yukon, Canada in May 2016. Linear regression (LR) and simple kriging (SK), combined with cross correlation and Bayesian model averaging, are used to interpolate estimates of snow water equivalent (SWE) from snow depth and density measurements. Snow distribution patterns are found to differ considerably between glaciers, highlighting strong inter- and intra-basin variability. Elevation is found to be the dominant control of the spatial distribution of SWE, but the relationship varies considerably between glaciers. A simple parameterization of wind redistribution is also a small but statistically significant predictor of SWE. The SWE estimated for one study glacier has a short range parameter (90 m) and both LR and SK estimate a winter balance of ~0.6 m w.e. but are poor predictors of SWE at measurement locations. The other two glaciers have longer SWE range parameters (~450 m) and due to differences in extrapolation, SK estimates are more than 0.1 m w.e. (up to 40%) lower than LR estimates. By using a Monte Carlo method to quantify the effects of various sources of uncertainty, we find that the interpolation of estimated values of SWE is a larger source of uncertainty than the assignment of snow density or than the representation of the SWE value within a terrain model grid cell. For our study glaciers, the total winter balance uncertainty ranges from 0.03 (8%) to 0.15 (54%) m w.e. depending primarily on the interpolation method. Despite the challenges associated with accurately and precisely estimating winter balance, our results are consistent with the previously reported regional accumulation gradient.

Scientific research is increasingly collaborative and globally distributed; research groups now rely on web- based scientific tools and data management systems to simplify their day-to-day collaborative workflows. However, such tools often lack seamless interfaces, requiring researchers to contend with manual data transfers, annotation and sharing. MyGeoHub is a web platform that supports out-of-the-box, seamless workflows involving data ingestion, metadata extraction, analysis, sharing and publication. MyGeoHub is built on the HUBzero cyberinfrastructure platform and adds general-purpose software building blocks (GABBs), for geospatial data management, visualization and analysis. A data management building block iData, processes geospatial files, extracting metadata for keyword and map-based search while enabling quick previews. iData is pervasive, allowing access through a web interface, scientific tools on MyGeoHub or even mobile field devices via a data service API. GABBs includes a Python map library as well as map widgets that in a few lines of code, generate complete geospatial visualization web interfaces for scientific tools. GABBs also includes powerful tools that can be used with no programming effort. The GeoBuilder tool provides an intuitive wizard for importing multi-variable, geo-located time series data (typical of sensor readings, GPS trackers) to build visualizations supporting data filtering and plotting. MyGeoHub has been used in tutorials at scientific conferences and educational activities for K-12 students. MyGeoHub is also constantly evolving; the recent addition of Jupyter and R Shiny notebook environments enable reproducible, richly interactive geospatial analyses and applications ranging from simple pre- processing to published tools. MyGeoHub is not a monolithic geospatial science gateway, instead it supports diverse needs ranging from just a feature-rich data management system, to complex scientific tools and workflows.

Climate change is a problem that involves science, economics, and politics. Particularly in the United States, political resistance to addressing climate change has been exacerbated by a concerted misinformation campaign against the basic science, a negative response to how the proposed solutions to climate change intersect with values. Scientists often propose more climate science education as a solution to the problem, but preliminary studies indicate that more science education does not necessarily reduce polarization on the topic (Kahan et al. 2012). Is there a way that we can better engage non-science students in topics related to climate change that improve their comprehension of the problem and its implications, overcoming polarization? In an existing political science course, “Do You Want to Build a Nation?”, we are testing a new digital world-building model based on resource development and consequent environmental and societal impacts. Students spend half the class building their nations based on their assigned ideology (i.e., socialist, absolute monarchy, libertarian) and the second half of the class negotiating with other nations to resolve global issues while remaining true to their ideologies. The course instructor, co-author Lennon, and ASU’s Center for Education Through eXploration have collaborated to design a digital world model based on resources linked to an adaptive decision-making environment that translates student policies into modifications to the digital world. The model tracks students’ exploration and justification of their nation’s policy choices. In the Fall 2017 offering of the course, we will investigate how this digital world model and scenarios built around it affect student learning outcomes. Specifically, we anticipate improved understanding of the policy trade-offs related to energy development, better understanding of the ways that different ideologies approach solutions to climate change, and that both will result in more realistic diplomatic negotiations in the latter half of the course. We will report on the technical details of how the digital world model and scenarios are constructed as well as how students responded to the scenario.

Microbial transformation and decomposition of organic matter in sediments constitutes one of the largest fluxes of carbon in marine environments. Mineralization of sedimentary organic matter by microorganisms results in selective degradation such that bioavailable or accessible compounds are rapidly metabolized while more recalcitrant, complex compounds are preserved and buried in sediment (Mahmoudi et al., 2017). Recent studies have found that the ability to use different carbon sources appears to vary among microorganisms, suggesting that the availability of certain pools of carbon can be specific to the taxa that utilize the pool. This implies that organic matter mineralization in marine environments may depend on the metabolic potential of the microbial populations that are present and active. The goal of our study was to investigate the extent to which organic matter availability and transformation may be species-specific using sediment from Guaymas Basin (Gulf of California). We carried out time-series incubations using bacterial isolates and sterilized sediment in the IsoCaRB system (Beaupre et al., 2016) which allowed us to measure the production rates and natural isotopic signatures (δ13C and Δ14C) of microbially-respired CO2. Separate incubations using two different marine bacterial isolates (Vibrio sp. and Pseudoalteromonas sp.) and sterilized Guaymas Basin sediment under oxic conditions showed that the rate and total quantity of organic matter metabolized by these two species differs. Approximately twice as much CO2 was collected during the Vibrio sp. incubation compared to the Pseudoalteromonas sp. incubation. Moreover, the rate at which organic matter was metabolized by the Vibrio sp. was much higher than the Pseudoalteromonas sp. indicating the intrinsic availability of organic matter in sediments may depend on the species that is present and active. Isotopic analyses of microbially respired CO2 will be used to constrain the type and age of organic matter that is accessible to each species. Moreover, molecular analysis of subsamples collected from each incubation will link carbon utilization with the underlying gene expression. Our study sheds light on the degree to which the metabolic capacities of microorganisms affect carbon transformation in sedimentary environments.

Hydrological distributed modeling is a key point for a comprehensive assessment of the feedback between the dynamics of the hydrological cycle, climate conditions, and land use. Such modeling results are markedly relevant in the fields of water resources management. Here TopModel (TOPography based hydrological MODEL) is employed for the hydrological modeling of an area in the Middle Magdalena Valley (MMV), a tropical basin located in Colombia. This study is located in the intertropical convergence zone and is characterized by special meteorological conditions, with fast water fluxes over the year. It has been subject to significant land use changes, as a result of intense economic activities, i.e., agriculture, energy and oil & gas production. The model employees a record of 12 years of: • Daily precipitation database from observed gauges • Daily evapotranspiration database from temperature data • Streamflow database as observed data from calibration Calibration is performed using data from 2000 to 2008, and validation is performed with data from 2009 to 2012. The Nash-Sutcliffe coefficient is used to assess the robustness of our calibration process.(values of this metric being 0.62 and 0.53, respectively for model calibration and validation). The results reveal high water storage capacity in the soil, and a marked subsurface runoff, consistent with the characteristics of the soil types in the regions. The calibrated model provides relevant indications about recharge in the region, which is important to quantify the interaction between surface water and groundwater, especially during the dry season, which is more relevant in climate-change and climate-variability scenarios.

Over the past three decades, large tracts of tropical forests have been converted to crop and pasturelands across southern Amazonia, largely to meet the increasing worldwide demand for protein. As the world’s population continue to grow and consume more protein per capita, forest conversion to grow more crops could be a potential solution to meet such demand. However, widespread deforestation is expected to negatively affect crop productivity via multiple pathways (e.g., thermal regulation, rainfall, local moisture, pest control, among others). To quantify how deforestation affects crop productivity, we modeled the relationship between forest amount and enhanced vegetation index (EVI—a proxy for crop productivity) during the soybean planting season across southern Amazonia. Our hypothesis that forest amount causes increased crop productivity received strong support. We found that the maximum MODIS-based EVI in soybean fields increased as a function of forest amount across three spatial-scales, 0.5 km, 1 km, 2 km, 5 km, 10 km, 15 km and 20 km. However, the strength of this relationship varied across years and with precipitation, but only at the local scale (e.g., 500 meters and 1 km radius). Our results highlight the importance of considering forests to design sustainable landscapes.

Shoreline change along the Eastern Atlantic shore of Virginia has been studied for the individual barrier islands but not as an integrated system. This study combines the Atlantic shoreline locations for eleven barrier islands obtained from LANDSAT 5, 7, and 8 images. Approximately 250 shoreline locations over a 24-year period from Jan 1990 to Dec 2014 were extracted from the digitized shoreline data at 338 transects. The resulting 338 by 250 matrix was analyzed by the empirical orthogonal function (EOF) technique. The first four principal components (PC) explained 86 percent of the sample variance. Since the data was not detrended, the first PC was the overall trend of the data with a discontinuity in 2004-2005. The 2004-2005 years included storm events and large shoreline changes. PCs 2 to 4 reflect the effects of El Nino events and tropical and non-tropical storms. Eigenvectors 1 to 4 all show the effects of the nine inlets in the island group. Eigenvector (EV) 1 explains 59 percent of the shoreline spatial variance and shows the largest changes at the northern and southern island ends. EVs 2 to 4 reflect the pattern of EV1 but at sequentially smaller percentages of the spatial variance. As a group, the eleven islands are losing ocean side shoreline. The lone exception is Hog Island. Sea level had the strongest correlation with the shoreline loss trend of PC1. The coefficient of determination was 0.41. The NAO and MEI also correlated with PC1 with correlations of determination of 0.05 and 0.12 respectively. These confidence level for the three factors was better than 99 percent. Sea level also correlated with PC3 and PC4. The PCs as a group show that the year intervals 2004-2005 and 2009-2010 had large effects on the shoreline change pattern for the island group. EVs 1 to 4 had the highest range of shoreline change at the island ends indicating the effect the changes of the inlets have on the adjacent islands. The smaller islands as a group had a higher level of eigenvector variance than the other eight islands. Sea level change is the major factor affecting the ocean shorelines of these islands. Continued sea level increase will facilitate loss of ocean shoreline for the barrier islands as a group.

The Cyclone Global Navigation Satellite System (CYGNSS) is a multi-satellite constellation that utilizes Global Positioning System (GPS) reflectometry to retrieve near-surface wind speeds over the ocean. While CYGNSS is primarily aimed at measuring wind speeds in tropical cyclones, our research has established that the mission may also provide valuable insight into the relationships between wind-driven surface fluxes and general tropical oceanic convection. Currently, we are examining organized tropical convection using a mixture of CYGNSS level 1 through level 3 data, IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), and other ancillary datasets (including buoys, GPM level 1 and 2 data, as well as ground-based radar). In addition, observing system experiments (OSEs) are being performed using hybrid three-dimensional variational assimilation to ingest CYGNSS observations into a limited-domain, convection-resolving model. Our focus for now is on case studies of convective evolution, but we will also report on progress toward statistical analysis of convection sampled by CYGNSS. Our working hypothesis is that the typical mature phase of organized tropical convection is marked by the development of a sharp gust-front boundary from an originally spatially broader but weaker wind speed change associated with precipitation. This increase in the wind gradient, which we demonstrate is observable by CYGNSS, likely helps to focus enhanced turbulent fluxes of convection-sustaining heat and moisture near the leading edge of the convective system where they are more easily ingested by the updraft. Progress on the testing and refinement of this hypothesis, using a mixture of observations and modeling, will be reported.

A major source of lithospheric stresses is believed to be in variations of surface topography and lithospheric density. The traditional approach to stress estimation is based on direct calculations of the Gravitational Potential Energy (GPE), the depth integrated density moment of the lithosphere column. GPE is highly sensitive to density structure which, however, is often poorly constrained. Density structure of the lithosphere may be refined using methods of gravity modeling. However, the resulted density models suffer from non-uniqueness of the inverse problem. An alternative approach is to directly estimate lithospheric stresses (depth integrated) from satellite gravimetry data. Satellite gravity gradient measurements by the ESA GOCE mission ensures a wealth of data for mapping lithospheric stresses if a link between data and stresses or GPE can be established theoretically. Following (Camelbeeck et al., 2013), we adopt the method that constrains lithospheric stresses by direct utilization of the gravity gradient tensor. For comparison, we use more traditional methods as well: (2) the filtered geoid approach (e.g., Chase et al., 2002; Coblentz et al., 2015), and (3) the direct thin-sheet approximation based on depth integration of density moment (e.g., Medvedev, 2016). Whereas the last two approaches (2)-(3) calculate GPE and utilize a computationally expensive finite element mechanical modeling to calculate stresses, the approach (1) uses a much simpler numerical treatment but requires simplifying assumptions that yet to be tested. We applied all methods to the North Atlantic region where reliable additional constraints are available and tested results against the World Stress Map.

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation.

We perform physics-based simulations of earthquake rupture propagation on geometrically complex strike-slip faults. We consider many different realization of the fault roughness and obtain heterogeneous stress fields by performing dynamic rupture simulation of large earthquakes. We calculate the Coulomb failure function (CFF) for all these realizations so that we can quantify zones of stress increase/shadows surrounding the main fault and compare our results to seismic catalogs. To do this comparison, we use relocated earthquake catalogs from Northern and Southern California. We specify the range of fault roughness parameters based on past observational studies. The Hurst exponent (H) varies in range from 0.5 to 1 and RMS height to wavelength ratio ( RMS deviation of a fault profile from planarity) has values between 10-2 to 10-3. For any realization of fault roughness, the Probability density function (PDF) values relative to the mean CFF change show a wider spread near the fault and this spread squeezes into a narrow band as we move away from fault. For lower value of RMS ratio ( 10-3), we see bigger zones of stress change near the hypocenter and for higher value of RMS ratio ( 10-2), we see alternate zones of stress increase/decrease surrounding the fault to have comparable lengths. We also couple short-term dynamic rupture simulation with long-term tectonic modelling. We do this by giving the stress output from one of the dynamic rupture simulation (of a single realization of fault roughness) to long term tectonic model (LTM) as initial condition and then run LTM over duration of seismic cycle. This short term and long term coupling enables us to understand how heterogeneous stresses due to fault geometry influence the dynamics of strain accumulation in the post-seismic and inter-seismic phase of seismic cycle.

Structural imaging using body-wave energy present in ambient seismic data remains a challenging task, largely because these wave modes are commonly much weaker than surface wave energy. In a number of situations body-wave energy has been extracted successfully; however, (nearly) all successful body-wave extraction and imaging approaches have focused on cross-correlation processing. While this is useful for interferometric purposes, it can also lead to the inclusion of unwanted noise events that dominate the resulting stack, leaving body-wave energy overpowered by the coherent noise. Conversely, wave-equation imaging can be applied directly on non-correlated ambient data that has been preprocessed to mitigate unwanted energy (i.e., surface waves, burst-like and electromechanical noise) to enhance body-wave arrivals. Following this approach, though, requires a significant preprocessing effort on often Terabytes of ambient seismic data, which is expensive and requires automation to be a feasible approach. In this work we outline an automated processing workflow designed to optimize body wave energy from an ambient seismic data set acquired on a large-N array at a mine site near Lalor Lake, Manitoba, Canada. We show that processing ambient seismic data in the recording domain, rather than the cross-correlation domain, allows us to mitigate energy that is inappropriate for body-wave imaging. We first develop a method for window selection that automatically identifies and removes data contaminated by coherent high-energy bursts. We then apply time- and frequency-domain debursting techniques to mitigate the effects of remaining strong amplitude and/or monochromatic energy without severely degrading the overall waveforms. After each processing step we implement a QC check to investigate improvements in the convergence rates - and the emergence of reflection events - in the cross-correlation plus stack waveforms over hour-long windows. Overall, the QC analyses suggest that automated preprocessing of ambient seismic recordings in the recording domain successfully mitigates unwanted coherent noise events in both the time and frequency domain. Accordingly, we assert that this method is beneficial for direct wave-equation imaging with ambient seismic recordings.

Nonmigrating tidal diagnostics of SABER temperature observations in the ionospheric dynamo region reveal a large amount of variability on time-scales of a few days to weeks. In this paper, we discuss the physical reasons for the observed short-term tidal variability using a novel approach based on Information theory and Bayesian statistics. We diagnose short-term tidal variability as a function of season, QBO, ENSO, and solar cycle and other drivers using time dependent probability density functions, Shannon entropy and Kullback-Leibler divergence. The statistical significance of the approach and its predictive capability is exemplified using SABER tidal diagnostics with emphasis on the responses to the QBO and solar cycle. Implications for F-region plasma density will be discussed.

Spectral entropy (Hs) is an index which can be used to measure the structural complexity of time series data. When a time series is made up of one periodic function, the Hs value becomes smaller, while Hs becomes larger when a time series is composed of several periodic functions. We hypothesized that this characteristic of the Hs could be used to quantify the water stress history of vegetation. For the ideal condition for which sufficient water is supplied to an agricultural crop or natural vegetation, there should be a single distinct phenological cycle represented in a vegetation index time series (e.g., NDVI and EVI). However, time series data for a vegetation area that repeatedly experiences water stress may include several fluctuations that can be observed in addition to the predominant phenological cycle. This is because the process of experiencing water stress and recovering from it generates small fluctuations in phenological characteristics. Consequently, the value of Hs increases when vegetation experiences several water shortages. Therefore, the Hs could be used as an indicator for water stress history. To test this hypothesis, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data for a natural area in comparison to a nearby sugarcane area in seasonally-dry western Costa Rica. In this presentation, we will illustrate the use of spectral entropy to evaluate the vegetative responses of natural vegetation (dry tropical forest) and sugarcane under three different irrigation techniques (center pivot irrigation, drip irrigation and flood irrigation). Through this comparative analysis, the utility of Hs as an indicator will be tested. Furthermore, crop response to the different irrigation methods will be discussed in terms of Hs, NDVI and yield.

The natural sound generated by an unmanned aerial vehicle is used in conjunction with tomography to remotely sense atmospheric temperature and wind profiles simultaneously. Sound fields recorded onboard the aircraft and by an array of microphones on the ground are compared and converted to sound speed estimates for the ray paths intersecting the intervening medium. Tomographic inversion is then used to transform these sound speed values into vertical cross-sections and 3D volumes of virtual temperature and wind vectors, which enables the atmosphere to be visualised and monitored over time up to altitudes of 1,200m and over baselines of up to 600m. This paper reports on results from two short campaigns during which 2D and 3D profiles of wind and temperature obtained in this way were compared to: measurements taken by co-located mid-range Doppler SODAR and LIDAR; and temperature measurements made by instruments carried by unmanned aircraft flying through the intervening atmosphere. Large eddy simulation of daytime atmospheric boundary layers were also used to examine the anticipated performance of the instruments and the nature of any errors. The observations obtained using all systems are shown to correspond closely.