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From 2011 to 2016, California experienced a millennial-intensity drought, generating high levels of tree mortality. Remote sensing has been used to monitor the long-term impacts of drought; however, discriminating dead from live trees in arid and semiarid deciduous woodlands is challenging. The goals of this study were to assess and map the spatial patterns of drought-induced tree mortality in a blue oak (Quercus douglasii) woodland, a highly drought-tolerant species forming savannas along the lower foothills surrounding California's Central Valley. Airborne hyperspectral imagery was used to identify the most important wavelength regions predicting drought-induced blue oak mortality. The best metric to predict canopy stress was a normalized ratio using the spectral bands 937.53 and 1100.08 nm with a correlation with tree mortality of R2 = 0.83. The image prediction of mortality for nine field plots found that 16 of 98 trees died (17.9%) during the drought. We further evaluated tree mortality in 82 independent plots, and we found the greatest image predictive accuracy for tree mortality between 1% and 10%. When applied at the landscape level, the regression-based index found mortality ranged from 1% to more than 51% in oak stands with an average mortality of 10% over the entire study region. In addition, tree mortality at landscape level showed higher tree mortality in blue oaks on south-facing aspects probably because of higher insolation rates and in sites with low potential for accumulated drainage.

We must be able to predict and mitigate against geomagnetically induced current (GIC) effects to minimize socio-economic impacts. This study employs the space weather modeling framework (SWMF) to model the geomagnetic response over Fennoscandia to the September 7-8, 2017 event. Of key importance to this study is the effects of spatial resolution in terms of regional forecasts and improved GIC modeling results. Therefore, we ran the model at comparatively low, medium, and high spatial resolutions. The virtual magnetometers from each model run are compared with observations from the IMAGE magnetometer network across various latitudes and over regional-scales. The virtual magnetometer data from the SWMF are coupled with a local ground conductivity model which is used to calculate the geoelectric field and estimate GICs in a Finnish natural gas pipeline. This investigation has lead to several important results in which higher resolution yielded: (1) more realistic amplitudes and timings of GICs, (2) higher amplitude geomagnetic disturbances across latitudes, and (3) increased regional variations in terms of differences between stations. Despite this, substorms remain a significant challenge to surface magnetic field prediction from global magnetohydrodynamic modeling. For example, in the presence of multiple large substorms, the associated large-amplitude depressions were not captured, which caused the largest model-data deviations. The results from this work are of key importance to both modelers and space weather operators. Particularly when the goal is to obtain improved regional forecasts of geomagnetic disturbances and/or more realistic estimates of the geoelectric field.

The electron fluxes at geostationary orbit measured by Geostationary Operational Environmental Satellite (GOES) 13, 14, and 15 spacecraft are modeled using system identification techniques. System identification, similar to machine learning, uses input-output data to train a model, which can then be used to provide forecasts. This study employs the nonlinear autoregressive moving average exogenous technique to deduce the electron flux models. The electron fluxes at geostationary orbit are known to vary in space and time, making it a spatiotemporal system, which complicates the modeling using system identification/machine learning approach. Therefore, the electron flux data are binned into 24 magnetic local time (MLT), and a separate model is developed for each of the 24 MLT bins. MLT models are developed for six of the GOES 13, 14, and 15 electron flux energy channels (75 keV, 150 keV, 275 keV, 475 keV, >800 keV, and >2 MeV). The models are assessed on separate test data by prediction efficiency (PE) and correlation coefficient (CC) and found these to vary by MLT and electron energy. The lowest energy of 75 keV at the midnight sector had a PE of 36.0 and CC of 59.3, which increased on the dayside to a PE of 66.9 and CC of 81.6. These metrics increased to the >2 MeV model, which had a low PE and CC of 63.0 and 81.8 on the nightside to a high of 80.3 and 90.8 on the dayside.

Faraday's law of induction is responsible for setting up a geoelectric field due to the variations in the geomagnetic field caused by ionospheric currents. This drives geomagnetically induced currents (GICs) which flow in large ground-based technological infrastructure such as high-voltage power lines. The geoelectric field is often a localized phenomenon exhibiting significant variations over spatial scales of only hundreds of kilometers. This is due to the complex spatiotemporal behavior of electrical currents flowing in the ionosphere and/or large gradients in the ground conductivity due to highly structured local geological properties. Over some regions, and during large storms, both of these effects become significant. In this study, we quantify the regional variability ofdB/dtusing closely placed IMAGE stations in northern Fennoscandia. The dependency between regional variability, solar wind conditions, and geomagnetic indices are also investigated. Finally, we assess the significance of spatial geomagnetic variations to modeling GICs across a transmission line. Key results from this study are as follows: (1) Regional geomagnetic disturbances are important in modeling GIC during strong storms; (2)dB/dtcan vary by several times up to a factor of three compared to the spatial average; (3)dB/dtand its regional variation is coupled to the energy deposited into the magnetosphere; and (4) regional variability can be more accurately captured and predicted from a local index as opposed to a global one. These results demonstrate the need for denser magnetometer networks at high latitudes where transmission lines extending hundreds of kilometers are present.

This research reports the views of Engineering and Technical Specialists (ETS or techreps) and their customers. The views were gathered from a variety of locations using group interviews and questionnaires. The questionnaires asked techreps to rate the Importance of various activities and the Time Spent on them. The customer questionnaire rated the Importance and Value Added of the activities. Although the focus is on the Naval Aviation Engineering Service Unit (NAESU), it also includes data from the Army, Air Force, and Navy Fleet. Techreps principal activities relate to on-the job training, classroom training, and maintenance advice and assistance. There are distinct differences between the priority and time spent on various activities by techreps from the four service groups. Customers, operations and maintenance personnel at various levels, believe techreps are essential to enable military personnel to operate and maintain the equipment assigned to their units. Both techreps and customers state that the need for techreps results from deficiencies in military training, assignment practices, lack of experienced personnel, inadequate technical data, parts shortages, sophistication of equipment, and other systemic problems. Techreps help compensate for mismatches in the overall logistics system. The report concludes with recommendations designed to improve the overall management of techreps. (MM)