Propagation of electromagnetic (EM) waves from radar and communication devices at sea can be significantly affected by the low-altitude atmospheric refractive conditions. Characterizing the spatial and temporal variability of refractivity is thus crucial to many important Navy and civilian applications. Surface layer models based on Monin-Obukhov Similarity Theory (MOST), such as the Navy Atmospheric Vertical Surface Layer Model (NAVSLaM), can be used to estimate the vertical refractive structure of the evaporation duct (ED) from measured or forecasted environmental parameters, assuming a horizontally-homogeneous stratified atmosphere. As air and sea surface temperature are two critical inputs to NAVSLaM, the rapid variation in sea surface temperature (SST) and air-sea temperature difference in the Gulf Stream region provides a relatively controlled environment for investigating the utility of NAVSLaM under inhomogeneous ducting conditions. In this paper, EM propagation in the ED over the Gulf Stream is investigated using measured data from the Coupled Air-Sea Processes and Electromagnetic-ducting Research (CASPER) East Coast experiment conducted off the coast of Duck, NC, during October-November of 2015. Measurements of the one-way propagation loss between a transmitter and receiving array as a function of the range were performed during ship crossings of the Gulf Stream. Concurrent and co-located meteorological and oceanographic measurements were also collected within the marine atmospheric boundary layer (MABL), including air temperature, SST, relative humidity, air pressure and wind speed to be used as inputs to NAVSLaM. The environmental conditions in the region of the Gulf Stream are used to model the low-altitude refractivity profiles as a function of height and range, which are input into the Advanced Propagation Model (APM) for predicting the EM propagation. Of particular focus is the effect of the rapid change in SST across the boundary between the warmer Gulf Stream and the colder surrounding ocean. EM measurements are compared with the simulated propagation loss based on the range-dependent refractivity profile predicted from NAVSLaM, as well as a best-fit Paulus-Jeske ED model. Both stable and unstable conditions encountered during the CASPER East experiment are investigated.

The aim of this study is two-fold: (1) measure solid Earth diurnal and semi-diurnal tidal constituents using non-parametric signal analysis, and (2) calculate the time delay of the Earth’s solid tidal response between two land-based geographic locations. For these goals, high-precision tidal signals were collected from borehole strainmeters at Plate Boundary Observatory (PBO) stations in Yellowstone, WY, USA and Sequim, WA, USA. Signals from synchronized microstrain time series sampled at 10 minute intervals from 1 January 2011 to 31 December 2015 were evaluated with power spectra and harmonic analysis with statistical F-testing. The phasing of the Earth’s tidal response for the two stations was also estimated with a solid Earth tidal model. Differences between the observed and modeled phases are investigated.

Tides are critical to coastal and oceanic processes. While tidal data are available readily, they are often corrupted by various sources of error. An automated, fast MATLAB toolbox is developed to clean-up tidal timeseries data from estuarine and oceanic locations corrupted by errors. This toolbox will immensely speed up delivery of quality-controlled tidal data. It will also reduce errors in quality control, which typically involves several manual tasks. The toolbox corrects poorly interpolated and noisy data, erroneous outliers, and instrumentation bias such as spurious jumps, drifts, spikes, and modulations in the true signal. Signal clean-up involves multiple stages. First, thresholds are imposed on higher order temporal derivatives of the signal to remove gross interpolations and noise saturated signal chunks, followed by a moving median threshold to remove outliers. Then the surviving signal is filtered into tidal, subtidal and long-period components, and the long-period component is subject to a maximal overlap discrete wavelet transformation, in which the transform coefficients corresponding to multi-scale edge features are removed. Subsequently, local information in the subtidal and tidal components is compared relative to the whole signal to correct spurious amplitude modulations and sudden biases. Consequently, these components are added to recover the uncorrupted signal, and large data gaps are filled with short term harmonic reconstruction. For estuarine locations, the correlation in the spectrogram between two nearby stations is initially used to quantify and remove river influence in the signal. Applications to datasets at multiple global locations demonstrate the value of the toolbox.

The variability of the middle atmosphere is driven by a variety of atmospheric waves covering various spatial and temporal scales. In particular, the northern winter mesosphere/ lower thermosphere at mid- and polar-latitudes shows a huge variability related to planetary waves, which can disturb the polar vortex leading to large scale coupling effects like sudden stratospheric warmings (SSWs) altering the vertical propagation conditions of tides and gravity waves. Here we are going to investigate and diagnose the short time variability of tides (several days) at the MLT using ground-based observations at mid and polar latitudes and data from NAVGEM-HA for selected periods. NAVGEM-HA provides information about the global structure of the zonal mean zonal and meridional wind and the zonal mean temperature as well as the tides. At mid- and high-latitudes the semi-diurnal tide (SW1 and SW2) is the dominating tidal wave during the winter season, which is also seen in meteor radar and lidar climatologies. Further, we analyze local meteor radar and lidar observations at Andenes (polar-latitude) and Juliusruh (mid-latitude) to diagnose the local amplitude and phase variability due to changes in the background mean winds caused by planetary waves and SSWs. We will show that the tidal phase (in UT) can drift significantly within several days and weeks. These local measurements are also compared to NAVGEM-HA applying the same diagnostic as to the observations. In addition to the winter time observations, we will also show results for the phase propagation of tides from summer periods.

Land use/land cover (LULC) change could adversely affect watershed health by elevating nutrients and sediment levels and intensifying the risk of flooding. In this study, a spatially-explicit LULC change modeling framework was coupled with the Chesapeake Bay Watershed Model (CBWM) to investigate the impact of LULC change on nutrients (total nitrogen and total phosphorous), sediment and runoff volume in the watersheds surrounding Virginia’s Shenandoah National Park, U.S. Four stakeholder-informed scenarios alongside a Recent Trends LULC change scenario were studied. The stakeholder-informed LULC change scenarios, which differed in consideration of future planning and population growth, were developed through several meetings with stakeholders. To develop the Recent Trends, the historical LULC trend from 2001 to 2011 was analyzed. Using 2011 as a baseline scenario, the spatio-temporal patterns of LULC change were estimated as influenced by physiographic and socio-economic drivers 50 years in the future (2061). The projected LULCs were fed into the CBWM to predict the change in average annual loading of nutrients, sediment and runoff volume. While the changes in loads at the full study area were not substantial (< 0.9%), changes became more pronounced at finer spatial scales. Expectedly, the LULC change scenario with ad-hoc planning and high population growth resulted in the largest increase in runoff volume. However, the scenario with ad-hoc planning and low population growth showed the largest increase in the simulated pollutants. This was because while this scenario projected less development, it projected more increases in agricultural LULCs that export more nutrients and sediment than other changing LULCs. This implied that sole land use planning based on urban development is not sufficient for watershed protection and agricultural LULCs need to be incorporated in concert in our future planning. This further suggested that land use planning plays a more critical role than population growth rate in water quality management. The results have implications for the Chesapeake Bay total maximum daily load and could help well-informed future land use planning and watershed protection by incorporating the impact of future LULC change on water quality and quantity.

Mangrove forests, occupying tropical and subtropical coastal areas, serve as coast protector, water filter, spot of attraction and an exceptional carbon reservoir. Despite these extraordinary ecological and economic functions, our understanding of the mangroves is limited because field survey is hard to conduct in the intertidal mangrove habitats. While satellite remote sensing provides good spatial and temporal coverages globally, data availability at the tropical latitudes is limited due to frequent cloud contamination. The quickly emerging unmanned aerial vehicle (UAV) technique enables data collection under almost all weather conditions, thus provides new opportunities. By mounting light detection and ranging (LiDAR) sensors on UAVs, the 3D structure of mangroves can be accurately characterized even at individual tree level. The basic tree parameters such as tree height and crown size can be easily extracted, which then enables the estimation of biomass, carbon stock and other important ecological indices. This study uses high density (>100 pt/m2) UAV-LiDAR data collected at four consecutive years to detect the growth rate and pattern of mangroves. The change detection is done at individual tree level. The dependence of mangrove growth pattern on tree species and clumpingness are analyzed. The output will allow scientists and environment managers to know mangroves better and to manage them effectively and efficiently.

Based on a large database of Wideband Imaging Camera (WIC), which is a part of Far Ultraviolet (FUV) instrument, onboard the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) satellite during 5 years between 2000 and 2005, we found a new morphological type of transpolar arcs (TPAs), which are identified as “nightside distorted TPAs”. The nightside ends of the dawnside (duskside) TPAs got distorted toward pre- (post-) midnight sector; These TPAs look like the shape of an alphabetical letter of “J” or “L”. We identified 17 nightside distorted TPAs from our database. The 12 events out of 17 nightside distorted TPA events were the dawnside TPA with the nightside end distorted toward the pre-midnight sector (“J”-shaped TPA), and the TPAs in the duskside, whose nightside parts got distorted toward the post-midnight sector, were found in the remnant 5 events (“L”-shaped TPA). Statistically, the nightside distorted TPAs can dominantly be found under the northward Interplanetary Magnetic Field (IMF) conditions. Furthermore, when the IMF-By component pointed to the dawnward (duskward) direction, the “J” (“L”)-shaped TPAs were dominantly observed, suggesting that the relation between the IMF-Bz and By orientations, and the locations of the nightside distorted TPAs is consistent with that between the IMF conditions and regular TPA previously reported. We also followed the time sequence of the nightside distorted TPA evolution with the IMAGE FUV-WIC imager and additional Polar UVI data. In most cases, the “J” and “L”-shaped TPAs started to grow from the nightside main auroral oval, and protruded to the dayside region with being distorted. In this presentation, we will introduce several selected cases of the nightside distorted TPA among our database, and discuss why and how the nightside end of the TPA became distorted toward pre- or post-midnight sectors based on the in-situ satellite nightside plasma sheet observations and the electron drift velocity distribution calculated utilizing a simple electromagnetic field model in magnetotail, together with the auroral imager data.

A hybrid Linear Programming (LP) and Nonlinear Programming (NLP) optimization model is developed for California’s hydropower operations. Built on top of Pyomo library, a high optimization modeling language in Python, the model can connect to several freely available, state-of-the-art solvers. In this model, fast evaluation of LP and detailed model representation of NLP are fully utilized. The hybrid model solves the same problem with linear approximation (a simplified objective function representation) and with NLP solver, where no simplification is made to objective function. Outputs from LP model are used as initial values (warmstart) for NLP model’s decision variables, which reduce number of iterations for convergence and so runtime. The model is capable of representing large network of hydropower plants that are in serial or parallel, or fixed and variable head plants. The model is used to evaluate effects of increased solar photovoltaic (PV) generation in California. California has a goal of generating electricity from renewable resources at least 33% by 2020, and 50% by 2030, and solar PV generation supplies most of renewable generation portfolio during daytime. This expanded use of solar PV changes generation pattern from one daily peak system to two daily peak system. Due to excess generation of solar PV, negative prices can occur during daytime. Therefore, evaluating effects of solar PV on hydropower operations and adapting to new conditions are essential.

Quantification of river flow is a significant component of water resource management needed to develop infrastructure, prepare for drought or flood, and ensure equitable resource use. Yet, these data have often been deficient in rural regions generally dependent on smaller river systems, especially in low- and middle- income countries, and in places where no remote gaging capabilities currently exist. A relationship between river flow and width has been previously demonstrated in attempts to record and monitor flow with only satellite-based measurements; however, those methods have focused on rivers with a width of > 30m and have shown extreme errors when compared to historical gaging records. A new technique for monitoring river flow remotely is presented here, which uses Manning’s equation for open channel flow coupled with width measurements from satellites. This technique allows for the creation of a remotely-sensed historical river flow record and uses the minimum number of ground-based measurements for the calibration of Manning’s equation. Cross-river depth profile, discharge, and slope are necessary for initial calibration. This method used four-band satellite images with 3m-resolution for width measurements, with images being adjusted for the Normalized Difference Water Index (NDWI) for better delineation of the water’s edge. Buffalo Creek, in Freeport, Pennsylvania and the Mutale River, in the Limpopo Province of South Africa, were used as study locations. An estimation of flow at each site was obtained using every available satellite image of those transects. Preliminary results indicated successful calibration of Manning’s equation for the individual river transects, and verification of this method yielded flow estimation with an average of 74% error at Buffalo Creek and 48% error at the Mutale River, when compared to historical data from gages present near the study sites. The primary challenge of using this method on smaller rivers is the determination of width from the satellite images. Automated techniques to delineate the water’s edge are presented as recommendations to increase accuracy. This method may be used as an additional tool in river flow monitoring for water resource management.

Gas hydrates have been reported to exist in marine sediments from various parts of the world ocean. The hydrates start decomposing soon after recovery of the sediments through coring operations due to changes in ambient pressure and temperature. This decomposition leads to changes in sedimentary structures, and thus complicates physical property related measurements of the sediments by conventional methods. In this study, we used a medical X-ray CT scanner to quickly scan the recovered cores, and then used raw data from the CT, and thus avoided image processing steps, to estimate porosity and density of the sediments. The raw data were in terms of CT numbers, which were obtained by drawing a circular region of interest (ROI) to cover most of the sediments visible in a cross section XCT image of the sediments. The data were weighted for relative contribution of liquid and solid in sediments before estimating porosity. On the other hand, density was estimated by using an average CT number that was automatically calculated by the Osirix software used for drawing the ROI on an XCT image, and by using a calibration equation based on a set of standards. Although some uncertainty in estimation of relative volumes of solid, liquid and gas could not be avoided, the results obtained by this new procedure were in good agreement with those obtained by conventional methods. Since porosity and density estimates by the new procedure can be made in a matter of minutes after core recovery, it can guide progress of coring operation and further processing of hydrate-bearing sediments.

Sea Level Rise is a global concern that has varying and potentially serious consequences on the local level. Although national policies and international frameworks can help to reduce the severity of sea level rise by limiting future emissions, in the current policy framework in the United States, local governments hold most of the responsibility for protecting their communities from flooding and the impacts of future sea level rise, with limited assistance from state and federal governments. Recognizing that local governments are largely driven by their community’s needs and desires, a survey of 500 persons affiliated with coastal communities across the east coast of the United States was conducted to identify public perceptions on the relative priority of sea level rise planning, components that should be included in local plans, protection priorities, funding mechanisms, methods to resolve conflict, and acceptable adaptation responses. The information from this survey was then provided to representatives from six local governments in a barrier island New Jersey to help identify its appropriateness and usefulness for local planning. In addition to discussing the key findings of the public survey, an overview of the responses from government officials will be presented, with an emphasis on comparing and contrasting the viewpoints of public officials and members of the public to help foster stronger collaborations among all members of a community (residents, businesses, utilities, governments and others) to help address local adaptation in light of this complex issue.

The Greater Cape Floristic Region (GCFR) of South Africa includes marine and terrestrial biomes with species diversity rivaling mega-diverse tropical rainforests in a compact area (300x700km). Extinction risk studies suggest that GCFR species are among the most vulnerable to climate change over the next 50 years. I present a scoping proposal commissioned by NASA to develop a field campaign to measure and monitor the distribution and abundance of biodiversity with new remotely-sensed data and the rich historical data in this region. I will summarize the central questions to be addressed by this field campaign and lay out the proposed study design to integrate satellite, airborne, and in situ data collection. Our plan centers around the collection of new hyperspectral imagery from AVIRIS-NG, PRISM, and HyTES spectrometers combined with the LVIS laser altimeter. These data will be collected at approximately 20 m spatial resolution across much of the GCFR and nearby aquatic and marine ecosystems. These data will then be combined with existing and new observations of the spatial distribution of community composition and functional traits to enable high resolution mapping and modeling of several essential biodiversity variables (EBVs) including species distributions, functional traits (including leaf properties), and three-dimensional canopy structure. Given the wealth of available independent in situ data available that can be brought to bear, the GCFR is an ideal system to fully evaluate the capabilities of remote-sensing technology to characterize biodiversity patterns across diverse landscapes in a relatively compact geographic area. In combination with the rich historical data and well-developed ecological understanding in this region, these new observations will enable detailed exploration into the drivers and mechanisms of change including the feedbacks from changing biodiversity to regional climate, disturbance, post-fire recovery, freshwater provisioning, and other ecosystem services.

Advising policy makers of ongoing geologic energy research is a key facet of enabling long-term energy security. If policy makers and their staffs do not hear from practicing scientists, they will likely rely on public opinion instead of making scientifically-informed choices about our energy future. As much of what the public hears related to geologic energy research has negative connotations (e.g., environmental harm, induced seismicity), this can lead to public pressure on policy makers to discourage all geologic energy research. Providing information to stakeholders to make informed decisions will help ground policy decisions in facts. Traditional geologic energy fuels (e.g., coal, oil, and natural gas) are still a necessary part of our energy portfolio, even as other energy sources are being developed. The Energy Investigations Program (EIP) at the Geological Survey of Alabama conducts research on all geologic energy resources that would affect the state and region, including fossil fuel resource assessment. EIP provides unbiased information about potential development, including projected costs, environmental impact, and broader economic impact. This research helps people make informed decisions about how (or even if) they want to develop these resources. Researchers must be sure that policy makers are aware of the broader role of geologic energy research as we move beyond old-school petroleum geology. For example, current work in EIP focuses on how carbon dioxide produced from burning fossil fuels might be captured and then either used or stored permanently (carbon capture, utilization, and storage, CCUS). CCUS includes permanent geologic sequestration and enhanced hydrocarbon recovery. Knowledge and support of continued geologic CCUS work is vital to long-term energy security. To ensure energy security, fossil fuels cannot be the only geologic energy solution. For example, compressed air energy storage and low-temperature geothermal energy are vital to understand as part of the energy revolution beyond fossil fuels. If decision-makers are unaware of these research efforts, it will be more difficult to gain support for a more balanced energy portfolio. By informing the public, legislators, and industry about these varied geologic energy sources, we enable a stronger energy future and bridge the interface between geoscience research and public policy.

There is much discussion in the literature about the need to balance abilities to respond to short-term and long-term risks among water systems. Yet our understanding of what contributes to our ability to respond to acute as well as long-term change is challenged by insufficient empirical work on water systems to date. We begin to fill this gap through an examination of the capacities of community water systems to absorb short-term shocks as well as to learn from them and act to reduce future risks. We investigated these capacities and how they interact using interviews (n=24) and a survey (n=85) of Connecticut community water systems. We found most systems have increased their capacity to respond to short-term risks and most have some (but differing levels) of capacity for learning from those experiences. However, few are preparing adequately for future risks. We present opportunities for reducing this gap.

Elevated nitrate concentrations in groundwater are observed in regions of intensive agriculture worldwide, threatening the safety of drinking-water production. Aquifers may contain geogenic reduced constituents, such as natural organic matter (NOM), pyrite, or biotite, facilitating aerobic respiration and denitrification. Because these electron donors are not replenished, the breakthrough of nitrate (and eventually dissolved oxygen) in production wells is only delayed. Frameworks of modeling nitrate fate and transport that assume constant rate coefficients of nitrate elimination cannot address the reduction of the aquifer’s denitrification potential by the reaction itself. We have tested several approaches of modeling the fate of dissolved oxygen and nitrate in aquifers, including multi-dimensional bioreactive transport models with dynamic abundances of aerobic and denitrifying bacteria, approaches neglecting the dynamics of biomass and dispersive mixing, and simple models based on an electron balance. We found that the primary control on the timing of nitrate breakthrough is the ratio of the bioavailable electron-donor content in the aquifer material to the electron-acceptor load in the infiltrating water. Combined spatial variability of groundwater velocities and electron-donor content can explain most of the spread in nitrate breakthrough, whereas kinetics of the reaction plays a minor role under most conditions. Our modeling study highlights the need for field surveys on joined physical and chemical heterogeneity of aquifers under the stress of pollutants that can react with the aquifer material.

Interest towards continuous cover forestry (CCF) has grown in recent years as it is considered more favorable from environmental perspectives than even-aged management. CCF could be particularly feasible on peatlands and other lowland soils as continuously maintaining a tree cover with significant evapotranspiration capacity could decrease the need for artificial drainage. Clear cutting, site preparation, and regular cleaning of drainage ditches increase greenhouse gas emissions and affect water quality by releasing sediment, nutrients and carbon to water bodies. Whereas even-aged management on peatlands relies on these intensive and environmentally adverse practices, regeneration in CCF forests would occur naturally and evapotranspiration of the tree stand would play a key role in maintaining drainage conditions. Partial harvest is an essential component of CCF and our study focuses on understanding its impacts on hydrology. The study site comprises a fertile drained peatland forest in Southern Finland, where three parallel sites were established in March 2016: (i) clear-cut with site preparation and seedling planting, (ii) partial harvest removing 75% of tree biomass, and (iii) control left untouched as reference. Data on ecosystem fluxes (Eddy covariance) and ground water depth were available from each site after the harvest and for a pre-treatment period of 6 years. In our attempt to understand the mechanisms behind observed changes after clear-cut and partial harvest, we applied a one-dimensional multi-layer multi-species soil-vegetation-atmosphere transfer model in conjunction with data analysis. The hydrology of each parallel site was simulated to explore the role of the amount and diversity of vegetation. Results suggested that on the partial harvest site the undergrowth of birch and spruce had the potential to partly compensate for the transpiration of the harvested pine, which dominated the stand before the treatment and limited the light received by the undergrowth trees. Such changes in vegetation-driven water balance components revealed by mechanistic modeling form the basis for understanding vegetation controls on growing season ground water depth, which is a key factor for the successful implementation of CCF in peatland forests.

Based on satellites monitoring of the X-Rays Precipitations (Solar Flares), Solar Proton Precipitations (Solar Proton Events), and Riometer absorption in association with F region plasma patches formed in the cusp and propagating into the polar cap, we estimate ionospheric D-Layer ionization caused by energetic electrons precipitations based on our Computational Model of D-Region Ion Production. Knowing D-Layer ionization mechanisms will allow us to monitor precipitating electron source rates based on routine ionospheric absorption measurements by the Canadian Geospace Observatory Riometer Network (GO-RIO).

Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000-2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead-times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices, derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean R2cv values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.