ALBERT

Description: We adapted Newton's Law of Cooling to model downstream water temperature change in response to stream-adjacent forest harvest on small and medium streams (average 327 ha in size) throughout the Oregon Coast Range, USA. The model requires measured stream gradient, width, depth and upstream control reach temperatures as inputs and contains two free parameters which were determined by fitting the model to measured stream temperature data. This model reproduces the measured downstream temperature responses to within 0.4 C ° for 15 of the 16 streams studied and provides insight into the physical sources of site-to-site variation among those responses. We also use the model to examine how the pre-to-post harvest change in daily maximum stream temperature depends on distance from the harvest reach. The model suggests that the pre-to-post harvest temperature change approximately 300  m downstream of the harvest will range from roughly 82% to less than 1% of that temperature change which occurred within the harvest reach, depending primarily on the downstream width, depth, and gradient. Using study-averaged values for these channel characteristics the model suggests that for a stream representative of those in the study, the temperature change approximately 300  m downstream of the harvest will be 56% of the temperature change which occurred within the harvest reach. This adapted Newton's Law of Cooling procedure represents a highly practical means for predicting stream temperature behavior downstream of timber harvests relative to conventional heat budget approaches, and is informative of the dominant processes affecting stream temperature. This article is protected by copyright. All rights reserved.

Description: We have fabricated novel controlled-geometry samples for the laser-heated diamond anvil cell (LHDAC) in which a transparent oxide layer (SiO 2 ) is sandwiched between two laser-absorbing layers (Ni) in a single, cohesive sample. The samples were mass manufactured (〉10 4 samples) using a combination of physical vapor deposition, photolithography, and wet and plasma etching. The double hot-plate arrangement of the samples, coupled with the chemical and spatial homogeneity of the laser-absorbing layers, addresses problems of spatial temperature heterogeneities encountered in previous studies where simple mechanical mixtures of transparent and opaque materials were used. Here we report thermal equations of state (EOS) for nickel to 100 GPa and 3000 K and stishovite to 50 GPa and 2400 K obtained using the LHDAC and in situ synchrotron x-ray micro-diffraction. We discuss the inner core composition and the stagnation of subducted slabs in the mantle based on our refined thermal EOS.

Description: Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of Greenhouse Gases (GHG's), we developed the first comprehensive monitoring systems of CO 2 emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012-2013 dormant season, showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ±0.23 MtC) compared to the Hestia CO 2 emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO 2 boundary inflow and to the different prior emissions ( i.e. ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO 2 emissions based on the ensemble mean and quartiles (5.26 - 5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.

Description: Abstract The South Central United States is a hot spot for anthropogenic methane (CH4) emissions, with contributions from the oil/gas (O&G) and animal agriculture sectors. During frontal weather events, airflow combines enhancements from these emissions into a large plume. In this study, we take CH4 and ethane (C2H6) observations from the Atmospheric Carbon and Transport‐America campaign and adjust O&G and animal agriculture emissions such that modeled CH4 and C2H6 enhancements match the observed plume. Results from the joint CH4‐C2H6 optimization indicate that emissions from the O&G sector are 1.8 ± 0.7 (2σ) times larger than EPA inventory estimates. These results match synthesis work from recent literature and reject the possibility that this increase compared to inventories is due to a potential bias in daytime‐only measurements of these facilities. Successful modeling from this study raises the possibility of using trace gas measurements along frontal crossings to solve for emissions in other regions of the United States.

Description: Abstract Production of coal and natural gas is responsible for one‐third of anthropogenic methane (CH4) emissions in the United States. Here we examine CH4 emissions from coal and natural gas production in southwestern Pennsylvania. Using a top‐down methodology combining measurements of CH4 and ethane we conclude that while EPA inventories appear to report emissions from coal accurately, emissions from unconventional natural gas are underreported in the region by a factor of 5 (±3). However, production‐scaled CH4 emissions from unconventional gas production in the Marcellus remain small compared to other basins due to its large production per well. After normalizing emissions by energy produced, total greenhouse gas emissions from Pennsylvania unconventional natural gas production produce half the carbon footprint compared to regionally produced coal, with carbon dioxide emissions from combustion being the dominant source of greenhouse gas emissions for both sources.

Description: Soil moisture is a critical variable in the water and energy cycles. The prediction of soil moisture patterns, especially at high spatial resolution, is challenging. This study tests the ability of a land surface hydrologic model (Flux-PIHM) to simulate high-resolution soil moisture patterns in the Shale Hills watershed (0.08 km 2 ) in central Pennsylvania. Locally measured variables including a soil map, soil parameters, a tree map, and lidar topographic data, all have been synthesized into Flux-PIHM to provide model inputs. The predicted 10-cm soil moisture patterns for fifteen individual days encompassing seven months in 2009 are compared with the observations from 61 soil moisture monitoring sites. Calibrated using only watershed-scale and a few point-based measurements, and driven by spatially uniform meteorological forcing, Flux-PIHM is able to simulate the observed macro spatial pattern of soil moisture at ~10 m resolution (spatial correlation coefficient ~ 0.6) and the day-to-day variation of this soil moisture pattern, although it underestimates the amplitude of the spatial variability and the mean soil moisture. Results show that the spatial distribution of soil hydraulic parameters has the dominant effect on the soil moisture spatial pattern. The surface topography and depth to bedrock also affect the soil moisture patterns in this watershed. Using the National Land Cover Database (NLCD) in place of a local tree survey map makes a negligible difference. Field measured soil type maps and soil type-specific hydraulic parameters significantly improve the predicted soil moisture pattern as compared to the most detailed national soils database (Soil Survey Geographic Database, or SSURGO, 30-m resolution). This article is protected by copyright. All rights reserved.

Description: Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine ( Sus scrofa ), using removal data from aerial gunning in 59 site/time-frame combinations (480-19,600 acres) throughout Oklahoma and Texas, U.S. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (〈50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (3+) within a time frame during which demographic changes are minimized (i.e., a closed population; ≤ 3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates. This article is protected by copyright. All rights reserved.

Description: Abstract This study systematically examines the regional uncertainties and biases in carbon dioxide (CO2) mole fractions from two of the state‐of‐the‐art global CO2 analysis products, namely the Copernicus Atmosphere Monitoring Service (CAMS) real‐time atmospheric analysis from the European Centre for Medium‐Range Weather Forecasts and the CarbonTracker Near‐Real Time (CT‐NRT) reanalysis from the National Oceanic and Atmospheric Administration, by evaluation against hundreds of hours of airborne in situ measurements from the summer 2016 and winter 2017 Atmospheric Carbon and Transport (ACT) ‐‐ America field campaigns. Both the CAMS and CT‐NRT analyses agree reasonably well with the independent ACT‐America airborne CO2 measurements in the free troposphere, with root‐mean‐square deviations (RMSDs) between analyses and observations generally between 1‐‐2 ppm, but show considerably larger uncertainties in the atmospheric boundary layer where the RMSDs exceed 8 ppm in the lowermost 1 km of the troposphere in summer. There are strong variations in accuracy and bias between seasons, and across three different subregions in the United States (Mid‐Atlantic, Midwest and South), with the largest uncertainties in the Mid‐Atlantic region in summer. Overall, the RMSDs of the CAMS and CT‐NRT analyses against airborne data are comparable to each other, and largely consistent with the differences between the two analyses. The current study provides uncertainty estimates for both analysis products over North America and suggests that these two independent estimates can be used to approximate regional CO2 analysis uncertainties. Both statistics are important in future studies in quantifying the uncertainties in regional CO2 mole fraction and flux estimates.

Description: Abstract Slow ecological processes challenge conservation. Short‐term variability can obscure the importance of slower processes that may ultimately determine the state of a system. Furthermore, management actions with slow responses can be hard to justify. One response to slow processes is to explicitly concentrate analysis on state dynamics. Here, we focus on identifying drivers of Northern Spotted Owl (Strix occidentalis caurina) territorial occupancy dynamics across 11 study areas spanning their geographic range and forecasting response to potential management actions. Competition with Barred Owls (Strix varia) has increased Spotted Owl territory extinction probabilities across all study areas and driven recent declines in Spotted Owl populations. Without management intervention, the Northern Spotted Owl subspecies will be extirpated from parts of its current range within decades. In the short term, Barred Owl removal can be effective. Over longer time spans, however, maintaining or improving habitat conditions can help promote the persistence of northern spotted owl populations. In most study areas, habitat effects on expected Northern Spotted Owl territorial occupancy are actually greater than the effects of competition from Barred Owls. This study suggests how intensive management actions (removal of a competitor) with rapid results can complement a slower management action (i.e., promoting forest succession).

Description: Contact rates vary widely among individuals in socially structured wildlife populations. Understanding the interplay of factors responsible for this variation is essential for planning effective disease management. Feral swine ( Sus scrofa ) are a socially structured species which pose an increasing threat to livestock and human health, and little is known about contact structure. We analyzed 11 GPS data sets from across the United States to understand the interplay of ecological and demographic factors on variation in co-location rates, a proxy for contact rates. Between-sounder contact rates strongly depended on the distance among home ranges (less contact among sounders separated by 〉2 km; negligible between sounders separated by 〉6 km), but other factors causing high clustering between groups of sounders also seemed apparent. Our results provide spatial parameters for targeted management actions, identify data gaps that could lead to improved management and provide insight on experimental design for quantitating contact rates and structure.