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Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM 2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM 2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM 2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

Control rod calibration experiment results for the Oregon State TRIGA® Reactor (OSTR) immediately following LEU conversion in 2008, and MCNP® 5 predicted rod worths from the 2008 LEU Conversion Safety Analysis Report (CSAR) are discussed. The reactivity worth of the four OSTR control rods is measured using the rod-pull method. Reactor power and period measurements in this method rely on the fission chamber power detector on the north side of the reflector. It is proposed that the location of the fission chamber and the neutron flux distribution in the core may result in an inaccurate reactor period measurement due to the asymmetry of the neutron flux distribution in the OSTR core. The asymmetry of the flux is believed to be more pronounced during super-criticality, resulting in errors in the time-of-power-rise measurements. As a result, control rod calibration experiments may under-predict or over-predict the reactivity worth of certain control rods. A time-independent Monte–Carlo method for the quantification of these effects is presented. Thermal flux maps at the core axial mid-plane are obtained from the model to inform discrepancies between predicted and observed results. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., "point-cloud predictors", and models developed using predictors extracted from pre-rasterized layers, i.e., "rasterized predictors". Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed. [ABSTRACT FROM AUTHOR]

The Columbia River Basalt Group (CRBG) is the youngest continental flood basalt province, proposed to be sourced from the deep-seated plume that currently resides underneath Yellowstone National Park. If so, the earliest erupted basalts from this province, such as those in the Picture Gorge Basalt (PGB), aid in understanding and modeling plume impingement and the subsequent evolution of basaltic volcanism. Using geochemical and isotopic data, this study explores potential mantle sources and magma evolution of the PGB. Long known geochemical signatures of the PGB include overall large ion lithophile element (LILE) enrichment and relative depletion of high field strength elements (HFSE) typical of other CRBG main-phase units. Basaltic samples of the PGB have 87Sr/86Sr ratios on the low end of the range displayed by other CRBG lavas and mantle-like δ18O values. The relatively strong enrichment of LILE and depletion of HFSE coupled with depleted isotopic signatures suggest a metasomatized upper mantle as the most likely magmatic source for the PGB. Previous geochemical modeling of the PGB utilized the composition of two high-MgO primitive dikes exposed in the northern portion of the Monument Dike swarm as parental melt. However, fractionation of these dike compositions cannot generate the compositional variability illustrated by basaltic lavas and dikes of the PGB. This study identifies a second potential parental PGB composition best represented by basaltic flows in the extended spatial distribution of the PGB. This composition also better reflects the lowest stratigraphic flows identified in the previously mapped extent of the PGB. Age data reveal that PGB lavas erupted first and throughout eruptions of main-phase CRBG units (Steens, Imnaha, Grande Ronde Basalt). Combining geochemical signals with these age data indicates cyclical patterns in the amounts of contributing mantle components. Eruption of PGB material occurred in two pulses, demonstrated by a ~0.4 Ma temporal gap in reported ages, 16.62 to 16.23 Ma. Coupling ages with observed geochemical signals, including relative elemental abundances of LILE, indicates increased influence of a more primitive, potentially plume-like source with time. [ABSTRACT FROM AUTHOR]

The health of coniferous forests in the western U.S. is under threat from mega-drought events, increasing vulnerability to insects, disease, and mortality. Forest densification resulting from fire exclusion increases these susceptibilities. Silvicultural treatments to reduce stand density and promote resilience to both fire and drought have been used to reduce these threats but there are few quantitative evaluations of treatment effectiveness. This proof-of-concept study focused on such an evaluation, using field and remote sensing metrics of mature ponderosa pine (Pinus ponderosa Doug. Laws) in central Oregon. Ground metrics included direct measures of transpiration (sapflow), branch and needle measures and chlorosis; drone imagery included thermal (TIR) and five-band spectra (R, G, B, Re, NIR). Thermal satellite imagery was derived from ECOSTRESS, a space-borne thermal sensor that is on-board the International Space Station (ISS). All metrics were compared over 2 days at a time of maximum seasonal drought stress (August). Tree water status in unthinned, light, and heavy thinning from below density reduction treatments was evaluated. Tree crowns in the heavy thin site had greater transpiration and were cooler than those in the unthinned site, while the light thin site was not significantly cooler than either unthinned or the heavy thin site. There was a poor correlation (Adj. R2 0.10–0.13) between remotely sensed stand temperature and stand-averaged transpiration, and tree level temperature and transpiration (Adj. R2 0.04–0.19). Morphological attributes such as greater needle chlorosis and reduced elongation growth supported transpirational indicators of tree drought stress. The multispectral indices CCI and NDRE, along with the NIR and B bands, show promise as proxies for crown temperature and transpiration, and may serve as a proof of concept for an approach to evaluate forest treatment effectiveness in reducing tree drought stress. [ABSTRACT FROM AUTHOR]

Uncrewed aircraft systems (UASs) with integrated light detection and ranging (lidar) technology are becoming an increasingly popular and efficient remote sensing method for mapping. Due to its quick deployment and comparatively inexpensive cost, uncrewed laser scanning (ULS) can be a desirable solution to conduct topographic surveys for areas sized on the order of square kilometers compared to the more prevalent and mature method of airborne laser scanning (ALS) used to map larger areas. This paper rigorously assesses the accuracy and quality of a ULS system with comparisons to terrestrial laser scanning (TLS) data, total station (TS) measurements, and Global Navigation Satellite System (GNSS) check points. Both the TLS and TS technologies are ideal for this assessment due to their high accuracy and precision. Data for this analysis were collected over a period of two days to map a landslide complex in Mulino, Oregon. Results show that the digital elevation model (DEM) produced from the ULS had overall vertical accuracies of approximately 6 and 13 cm at 95% confidence when compared to the TS cross-sections for the road surface only and road and vegetated surfaces, respectively. When compared to the TLS data, overall biases of −2.4, 1.1, and −2.7 cm were observed in X, Y, and Z with a 3D RMS difference of 8.8 cm. Additional qualitative and quantitative assessments discussed in this paper show that ULS can provide highly accurate topographic data, which can be used for a wide variety of applications. However, further research could improve the overall accuracy and efficiency of the cloud-to-cloud swath adjustment and calibration processes for georeferencing the ULS point cloud. [ABSTRACT FROM AUTHOR]

There is scarce information regarding the interactions between young tree water uptake and the environment in water-limited ecosystems. This study was conducted in a semiarid rangeland ecosystem in central Oregon, Pacific Northwest Region, USA. We measured the tree transpiration of western juniper (Juniperus occidentalis) saplings using the stem heat balance (SHB) method. We analyzed the correlation between transpiration and environmental factors affecting the saplings' water use from May to October for 2017, 2018, 2019, 2021, and 2022. The study results showed that total annual precipitation for all but one year was below the long-term (2005 to 2022) mean precipitation value of 307 mm for the study site. Significantly higher transpiration rates were observed in the wet vs. dry years. The highest monthly averaged transpiration rates (2.95 L d−1) were obtained in August during the above-average precipitation year (2017). Peak transpiration rates for the below-average precipitation years were generally reached in June or July, ranging from 0.91 to 1.65 L d−1. The seasonal response of transpiration to different environmental factors varied. For all years, vapor pressure deficit (VPD), solar radiation (SR), and air temperature (AT) showed a positive correlation with transpiration, whereas precipitation (Pr) and relative humidity (RH) indicated a negative correlation with transpiration. Soil moisture (SM) and soil temperature (ST) positively correlated with transpiration for most years. A strong association between VPD and transpiration was observed during the wettest (2017; 327 mm) and driest (2021; 198 mm) years. Results from this study add to the limited literature on sapling transpiration and can contribute to the improved management of cool-climate rangeland ecosystems through an enhanced understanding of water use by young-stage trees and its potential impacts on the water balance of restored juniper landscapes. [ABSTRACT FROM AUTHOR]

We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire. Our goal was to develop an adaptable simulation platform for anticipating risk-mitigation tradeoffs in a fire-prone wildland–urban interface (WUI) facing conditions outside the bounds of experience. We describe how five social and ecological system (SES) submodels interact over time and space to generate highly variable alternative futures even within the same scenario as stochastic elements in simulated wildfire, succession, and landowner decisions create large sets of unique, path-dependent futures for analysis. We applied the modeling system to an 815 km2 study area in western Oregon at a sub-taxlot parcel grain and annual timestep, generating hundreds of alternative futures for 2007–2056 (50 years) to explore how WUI communities facing compound risks from increasing wildfire and expanding periurban development can situate and assess alternative risk management approaches in their localized SES context. The ability to link trends and uncertainties across many futures to processes and events that unfold in individual futures is central to the modeling system. By contrasting selected alternative futures, we illustrate how assessing simulated feedbacks between wildfire and other SES processes can identify tradeoffs and leverage points in fire-prone WUI landscapes. Assessments include a detailed "post-mortem" of a rare, extreme wildfire event, and uncovered, unexpected stabilizing feedbacks from treatment costs that reduced the effectiveness of agent responses to signs of increasing risk. [ABSTRACT FROM AUTHOR]

Max-stable process (MSP) models can be fit to data collected over a spatial domain to estimate areal-based exceedances while accounting for spatial dependence in extremes. They have theoretical grounding within the framework of extreme value theory (EVT). In this work, we fit MSP models to three-day duration cool season precipitation maxima in the Willamette River Basin (WRB) of Oregon and to 48 h mid-latitude cyclone precipitation annual maxima in the Upper Trinity River Basin (TRB) of Texas. In total, 14 MSP models were fit (seven based on the WRB data and seven based on the TRB data). These MSP model fits were developed and applied to explore how user choices of study area sampling density, gage extent, and model fitting method impact areal precipitation-frequency calculations. The impacts of gage density were also evaluated. The development of each MSP involved the application of a recently introduced trend surface modeling methodology. Significant reductions in computing times were achieved, with little loss in accuracy, applying random sample subsets rather than the entire grid when calculating areal exceedances for the Cougar dam study area in the WRB. Explorations of gage extent revealed poor consistency among the TRB MSPs with modeling the generalized extreme value (GEV) marginal distribution scale parameter. The gauge density study revealed the robustness of the trend surface modeling methodology. Regardless of the fitting method, the final GEV shape parameter estimates for all fourteen MSPs were greater than their prescribed initial values which were obtained from spatial GEV fits that assumed independence among the extremes. When two MSP models only differed by their selected fitting method, notable differences were observed with their dependence and trend surface parameter estimates and resulting areal exceedances calculations. [ABSTRACT FROM AUTHOR]

An unbiased MACA CMIP5 ensemble that optimized calculation of the growing season average temperature (GST) viticulture climate classification index throughout Northern California's Fort Ross-Seaview (FRS), Los Carneros (LC), Petaluma Gap (PG), and Russian River Valley (RRV) American Viticultural Areas (AVAs) was applied to compute the GST index and Pinot noir specific applications of the grapevine sugar ripeness (GSR) model on a mean decadal basis from the 1950s to the 2090s using RCP4.5 and RCP8.5 projections of minimum and maximum daily temperature. From the 1950s to the 2090s, a 2.1/3.6, 2.4/4.2, 2.3/4.0, 2.3/4.0, and 2.3/4.0 °C increase in the GST index and a rate advance of 1.3/1.9, 1.1/1.8, 1.3/2.0, 1.2/1.9, and 1.2/1.9 days a decade was computed for FRS, LC, PG, RRV, and across all four AVAs while using the RCP4.5/RCP8.5 climate projections, respectively. The GST index and GSR model calculations were highly correlated across both climate projections and their fitted models were used to update the Pinot noir specific upper bound for the GST index throughout each AVA using a published optimal harvest window for the northern hemisphere. At a 220 g/L target sugar concentration, the updated upper bound was 17.6, 17.5, 17.6, 17.5, and 17.6 °C for FRS, LC, PG, RRV, and across all four AVAs. For a 240 g/L sugar concentration, it was 17.9, 17.8, 17.9, 17.8, and 17.9 °C. The results from this study together with comparable results recently reported for the Willamette Valley AVA of Oregon using a different downscaled CMIP5 model archive suggest spatial invariance, albeit sugar concentration dependent, for the updated Pinot noir specific upper bound for the GST climate index. [ABSTRACT FROM AUTHOR]

The existing Scoggins Dam and reservoir are in Washington County, Oregon, and the title is held by the U.S. Bureau of Reclamation (Reclamation). Reclamation has previously identified dam safety concerns related to the existing embankment dam. Regional project sponsors, including Clean Water Services, have identified the need for expanded storage capacity in the reservoir to meet growing water demands and address water quality issues in the Tualatin River downstream of the dam. As part of efforts to resolve dam safety issues and increase the water storage in the reservoir, a comprehensive feasibility level design of a new 185-foot-high Roller Compacted Concrete (RCC) dam. Extraordinary seismic hazards have been identified in the region associated with the Cascadia Subduction Zone (CSZ). Further, any dam alternative carried forward for funding, final design, and construction will have to meet the Public Protection Guidelines (PPG) of Reclamation that require a formal quantitative risk analysis. A risk-informed design approach was adopted to configure the layout and cross-section properties of the dam. A multi-phase site characterization program and preliminary RCC mix design program were performed to support the design. In addition, models were developed, and an extensive suite of both (two-dimensional) 2D and (three-dimensional) 3D structural analyses were performed for seismic loadings with total durations of over 200 s, strong shaking of over 140 s, and peak ground accelerations of over 2 gravitational accelerations (g) (up to 50,000-year return period event). This paper describes the feasibility design configuration of the dam, including the seismic hazard characterization, structural analysis models, and seismic response modeling results. The expected performance of the dam relative to the risk-informed design criteria and Reclamation PPGs will be generally described. [ABSTRACT FROM AUTHOR]

Quantifying above-ground biomass changes, ΔAGB, is key for understanding carbon dynamics. National Forest Inventories, NFIs, aims at providing precise estimates of ΔAGB relying on model-assisted estimators that incorporate auxiliary information to reduce uncertainty. Poststratification estimators, PS, are commonly used for this task. Recently proposed endogenous poststratification, EPS, methods have the potential to improve the precision of PS estimates of ΔAGB. Using the state of Oregon, USA, as a testing area, we developed a formal comparison between three EPS methods, traditional PS estimators used in the region, and the Horvitz-Thompson, HT, estimator. Results showed that gains in performance with respect to the HT estimator were 9.71% to 19.22% larger for EPS than for PS. Furthermore, EPS methods easily accommodated a large number of auxiliary variables, and the inclusion of independent predictions of ΔAGB as an additional auxiliary variable resulted in further gains in performance. [ABSTRACT FROM AUTHOR]

Plant fractions have a diversity of biomolecules that can be used to make complicated reactions for the bioactive fabrication of metal nanoparticles (NPs), in addition to being beneficial as antioxidant medications or dietary supplements. The current study shows that Urtica dioica (UD) and biologically synthesized silver nanoparticles (AgNPs) of UD have antibacterial and antioxidant properties against bacteria (Escherichia coli and Pseudomonas putida) and Drosophila melanogaster (Oregon R+). According to their ability to scavenge free radicals, DPPH, ABTS, TFC, and TPC initially estimated the antioxidant potential of UD and UD AgNPs. The fabricated AgNPs were analyzed (UV–Vis, FTIR, EDS, and SEM) to determine the functional groups (alcohol, carboxylic acids, phenol, proteins, and aldehydes) and to observe the shape (agglomerated crystalline and rod-shaped structure). The disc diffusion method was used to test the antimicrobial properties of synthesized Ag-NPs against E. coli and P. putida. For 24 to 120 h, newly enclosed flies and third instar larvae of Drosophila were treated with UD and UD AgNPs. After exposure, tests for biochemical effects (acetylcholinesterase inhibition and protein estimation assays), cytotoxicity (dye exclusion), and behavioral effects (jumping and climbing assays) were conducted. The results showed that nanoparticles were found to have potent antimicrobial activity against all microbial strains tested at various concentrations. In this regard, ethno-medicinal characteristics exhibit a similar impact in D. melanogaster, showing (p < 0.05) significantly decreased cellular toxicity (trypan blue dye), enhanced biochemical markers (AChE efficacy and proteotoxicity), and improved behavioral patterns in the organism treated with UD AgNPs, especially in comparison to UD extract. The results of this study may help in the utilization of specific plants as reliable sources of natural antioxidants that may have been beneficial in the synthesis of metallic NPs, which aids in the production of nanomedicine and other therapeutic applications. [ABSTRACT FROM AUTHOR]

Drosophila melanogaster (the fruit fly) is a valuable experimental platform for modeling host–pathogen interactions. It is also commonly used to define innate immunity pathways and to understand the mechanisms of both host tolerance to commensal microbiota and response to pathogenic agents. Herein, we investigate how the host response to bacterial infection is mirrored in the expression of genes of Imd and Toll pathways when D. melanogaster strains with different γCOP genetic backgrounds are infected with Pseudomonas aeruginosa ATCC 27853. Using microarray technology, we have interrogated the whole-body transcriptome of infected versus uninfected fruit fly males with three specific genotypes, namely wild-type Oregon, γCOPS057302/TM6B and γCOP14a/γCOP14a. While the expression of genes pertaining to Imd and Toll is not significantly modulated by P. aeruginosa infection in Oregon males, many of the components of these cascades are up- or downregulated in both infected and uninfected γCOPS057302/TM6B and γCOP14a/γCOP14a males. Thus, our results suggest that a γCOP genetic background modulates the gene expression profiles of Imd and Toll cascades involved in the innate immune response of D. melanogaster, inducing the occurrence of immunological dysfunctions in γCOP mutants. [ABSTRACT FROM AUTHOR]

A broad understanding of local geology and hydrologic processes is important for effective water resources management. The objectives of this project were to characterize the hydrogeologic framework of the Oak Creek Watershed (OCW) geographical area and examine the connections between surface water and groundwater at selected locations along the main stem of Oak Creek. The OCW area comprises the Siletz River Volcanic (SRV) Formation in the upper portion of the watershed and sedimentary rock formations in the valley. Past hydrologic and geologic studies and our field measurement data were synthesized to create a hydrogeologic framework of the watershed, including a geologic interpretation and a conceptual model of shallow, deep, and lateral groundwater flow throughout the OCW. The highly permeable geology of the SRV formation juxtaposed against the Willamette Basin's sedimentary geology creates areas of opposing groundwater flow characteristics (e.g., hydraulic conductivity) in the watershed. The Corvallis Fault is the primary interface between these two zones and generally acts as a hydraulic barrier, deflecting groundwater flow just upstream of the fault interface. The extreme angle of the Corvallis Fault and adjacent less permeable sedimentary geology might facilitate subsurface bulk water storage in selected locations. The stream-aquifer relationships investigated showed gaining conditions are prominent in the upper watershed's northern volcanic region and transition into neutral and losing conditions in the downstream southern sedimentary region in the valley. Agriculture irrigation seepage in the valley appeared to contribute to streamflow gaining conditions. Results from this case study contribute critical information toward enhancing understanding of local hydrogeologic features and potential for improved SW-GW resources management in areas near coastal ranges such as those found in the Pacific Northwest, USA. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: Micro-Raman spectroscopy has been proved to be a quick technique for the study of minerals showing asbestiform morphology, in particular for identification of those ascribing to the phases defined by Law "asbestos". Moreover, information about the crystal surface—free or occupied by crystalline or amorphous particles—can be at the same time obtained, being this last feature important for the interaction of the fibers with the biological medium. Micro-Raman spectroscopy has been applied to fibrous minerals regulated as "asbestos"—anthophyllite, actinolite, amosite, crocidolite, tremolite, and chrysotile—responsible of severe diseases affecting mainly, but not only, the respiratory system. The technique proved to be powerful in the identification of the mineral phase and in the recognition of particles of carbonaceous materials (CMs) lying on the "asbestos" fibers surface. Also, erionite, a zeolite mineral, from different outcrops has been analyzed. To erionite has been ascribed the peak of mesothelioma noticed in Cappadocia (Turkey) during the 1970s. On the fibers, micro-Raman spectroscopy allowed to recognize many grains, micrometric in size, of iron oxy-hydroxides or potassium iron sulphate, in erionite from Oregon, or particles of CMs, in erionite from North Dakota, lying on the crystal surface. Raman spectroscopy appears therefore to be the technique allowing, without preparation of the sample, a complete characterization of the minerals and of the associated phases. [ABSTRACT FROM AUTHOR]

We present a systems modeling approach to the development of a place-based ecohydrological model. The conceptual model is calibrated to a variety of existing observations, taken in watershed 10 (WS10) at the HJ Andrews Experimental Forest (HJA) in Oregon, USA, a long term ecological research (LTER) site with a long history of catchment-scale data collection. The modeling framework was designed to help document and evaluate an evolving understanding of catchment processing of water, nitrogen, and carbon that has developed over the many years of on-going research at the site. We use the dynamic model to capture the temporal variation in the N and C budgets and to evaluate how different components of the complex system may control the retention and release of N in this pristine forested landscape. Results indicate that the relative roles of multiple competing controls on N change seasonally, between periods of wet/dry and growth/senescence. The model represents a communication strategy to facilitate dialog between disciplinary experimentalists and modelers, to produce a more complete picture of nitrogen cycling in the region. We view this explicit development of complete, yet conceptually simplified models as a useful and important way to evaluate complex environmental dynamics. [ABSTRACT FROM AUTHOR]

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km 2 ) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90 % . Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development. [ABSTRACT FROM AUTHOR]

Climate change is leading to increased drought intensity and fire frequency, creating early-successional landscapes with novel disturbance–recovery dynamics. In the Klamath Mountains of northwestern California and southwestern Oregon, early-successional interactions between nitrogen (N)-fixing shrubs (Ceanothus spp.) and long-lived conifers (Douglas-fir) are especially important determinants of forest development. We sampled post-fire vegetation and soil biogeochemistry in 57 plots along gradients of time since fire (7–28 years) and climatic water deficit (aridity). We found that Ceanothus biomass increased, and Douglas-fir biomass decreased with increasing aridity. High aridity and Ceanothus biomass interacted with lower soil C:N more than either factor alone. Ceanothus biomass was initially high after fire and declined with time, suggesting a large initial pulse of N-fixation that could enhance N availability for establishing Douglas-fir. We conclude that future increases in aridity and wildfire frequency will likely limit post-fire Douglas-fir establishment, though Ceanothus may ameliorate some of these impacts through benefits to microclimate and soils. Results from this study contribute to our understanding of the effects of climate change and wildfires on interspecific interactions and forest dynamics. Management seeking to accelerate forest recovery after high-severity fire should emphasize early-successional conifer establishment while maintaining N-fixing shrubs to enhance soil fertility. [ABSTRACT FROM AUTHOR]

Real-time process metrics are standard for the majority of fermentation-based industries but have not been widely adopted by the wine industry. In this study, replicate fermentations were conducted with temperature as the main process parameter and assessed via in-line Oxidation Reduction Potential (ORP) probes and at-line profiling of phenolics compounds by UV-Vis spectroscopy. The California and Oregon vineyards used in this study displayed consistent vinification outcomes over five vintages and are representative of sites producing faster- and slower-fermenting musts. The selected sites have been previously characterized by fermentation kinetics, elemental profile, phenolics, and sensory analysis. ORP probes were integrated into individual fermentors to record how ORP changed throughout the fermentation process. The ORP profiles generally followed expected trends with deviations revealing previously undetectable process differences between sites and replicates. Site-specific differences were also observed in phenolic and anthocyanin extraction. Elemental composition was also analyzed for each vineyard, revealing distinctive profiles that correlated with the fermentation kinetics and may influence the redox status of these wines. The rapid ORP responses observed related to winemaking decisions and yeast activity suggest ORP is a useful process parameter that should be tracked in addition to Brix, temperature, and phenolics extraction for monitoring fermentations. [ABSTRACT FROM AUTHOR]

Optimized wildfire risk reduction strategies are generally not resilient in the event of unanticipated, or very rare events, presenting a hazard in risk assessments which otherwise rely on actuarial, mean-based statistics to characterize risk. This hazard of actuarial approaches to wildfire risk is perhaps particularly evident for infrequent fire regimes such as those in the temperate forests west of the Cascade Range crest in Oregon and Washington, USA ("Westside"), where fire return intervals often exceed 200 years but where fires can be extremely intense and devastating. In this study, we used wildfire simulations and building location data to evaluate community wildfire exposure and identify plausible disasters that are not based on typical mean-based statistical approaches. We compared the location and magnitude of simulated disasters to historical disasters (1984–2020) in order to characterize plausible surprises which could inform future wildfire risk reduction planning. Results indicate that nearly half of communities are vulnerable to a future disaster, that the magnitude of plausible disasters exceeds any recent historical events, and that ignitions on private land are most likely to result in very high community exposure. Our methods, in combination with more typical actuarial characterizations, provide a way to support investment in and communication with communities exposed to low-probability, high-consequence wildfires. [ABSTRACT FROM AUTHOR]

Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground-based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study, we aim at estimating the SCW by fusing information extracted from the radar imagery acquired with the Sentinel-1 constellation, aerial laser scanning, and meteorological data. To describe the change of radar backscatter with moisture, we focused on six forest stands in the H.J. Andrews experimental forest in central Oregon, as well as four clear cut areas and one golf course, over the summers of 2015–2017. We found significant relationships when we executed the analysis on radar images in which individual tree crowns were delineated from lidar, as opposed to SCW estimated from individual pixel backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. dry periods (no rain for >1 h), but these effects only last for roughly 30 min after the end of a rain event. We developed a predictive model for SCW using the radar images acquired at dawn, and proved the capability of space-based radar systems to provide information for estimation of the canopy moisture under conditions of fresh rainfall during the dry season. [ABSTRACT FROM AUTHOR]

Exposure to fine particulate matter (PM2.5) has well-established systemic human health effects due in part to the chemical components associated with these exposures. Oxidative stress is a hypothesized mechanism for the health effects associated with PM2.5 exposures. The oxidative potential of PM2.5 has recently been suggested as a metric that is more indicative of human health effects than the routinely measured PM2.5 concentration. The purpose of this study was to analyze and compare the oxidative potential and elemental composition of PM2.5 collected at two locations during different seasons. PM2.5 was collected onto PTFE-coated filters (n = 16) along two highways in central Oregon, USA in the Winter (January) and Summer (July/August). PM2.5 was extracted from each filter via sonication in methanol. An aliquot of the extraction solution was used to measure oxidative potential using the dithiothreitol (DTT) assay. An additional aliquot underwent analysis via inductively coupled plasma—mass spectrometry (ICP-MS) to quantify elements (n = 20). Differences in PM2.5 elemental composition were observed between locations and seasons as well as between days in the same season. Overall, concentrations were highest in the winter samples but the contribution to total PM2.5 mass was higher for elements in the summer. Notably, the oxidative potential (nM DTT consumed/µg PM2.5/min) differed between seasons with summer samples having nearly a two-fold increase when compared to the winter. Significant negative correlations that were observed between DTT consumption and several elements as well as with PM2.5 mass but these findings were dependent on if the data was normalized by PM2.5 mass. This research adds to the growing evidence and justification for investigating the oxidative potential and composition of PM2.5 while also highlighting the seasonal variability of these factors. [ABSTRACT FROM AUTHOR]

Industry-specific tools for analyzing and optimizing the design of wave energy converters (WECs) and associated power systems are essential to advancing marine renewable energy. This study aims to quantify the influence of phase information on the device power output of a virtual WEC array. We run the phase-resolving wave model FUNWAVE-TVD (Total Variation Diminishing) to generate directional waves at the PacWave South site offshore from Newport, Oregon, where future WECs are expected to be installed for testing. The two broad cases presented correspond to mean wave climates during warm months (March–August) and cold months (September–February). FUNWAVE-TVD time series of sea-surface elevation are then used in WEC-Sim, a time domain numerical model, to simulate the hydrodynamic response of each device in the array and estimate their power output. For comparison, WEC-Sim is also run with wave energy spectra calculated from the FUNWAVE-TVD simulations, which do not retain phase information, and with wave spectra computed using the phase-averaged model Simulating WAves Nearshore (SWAN). The use of spectral data in WEC-Sim requires a conversion from frequency to time domain by means of random superposition of wave components, which are not necessarily consistent because of the linear assumption implicit in this method. Thus, power response is characterized by multiple realizations of the wave climates. [ABSTRACT FROM AUTHOR]

Research Highlights: In this longitudinal study, we explore the impacts of changing atmospheric composition and increasing aridity on the radial growth rates of western juniper (WJ; Juniperus occidentalis Hook). Since we sampled from study locations with minimal human agency, we can partially control for confounding influences on radial growth (e.g., grazing and logging) and better isolate the relationships between radial growth and climatic conditions. Background and Objectives: Our primary objective is to determine if carbon dioxide (CO2) enrichment continues to be a primary driving force for a tree species positively affected by increasing CO2 levels circa the late 1990s. Materials and Methods: We collected data from mature WJ trees on four minimally disturbed study sites in central Oregon and compared standardized radial growth rates to climatic conditions from 1905–2017 using correlation, moving-interval correlation, and regression techniques. Results: We found the primary climate driver of radial growth for WJ is antecedent moisture over a period of several months prior to and including the current growing season. Further, the moving-interval correlations revealed that these relationships are highly stable through time. Despite a trend toward increasing aridity manifested through significant increases in maximum temperatures during the summer growing season, WJ radial growth post-1960 exceeds growth pre-1960, especially during drought years. Our results support prior conclusions that increasing atmospheric CO2 increases water-use efficiency for this semiarid species, which allows the trees to continue to grow during climatic periods negatively associated with radial growth. Conclusions: Recent studies have shown that semiarid ecosystems are important for understanding global variations in carbon uptake from the atmosphere. As WJ woodlands cover an extensive region in western North America and have undergone rapid expansion during the 20th and 21st centuries, they may become an increasingly important carbon sink. [ABSTRACT FROM AUTHOR]

Understanding change in forest carbon (C) is important for devising strategies to reduce emissions of greenhouse gases. National forest inventories (NFIs) are important to meet international accounting goals, but data are often incomplete going back in time, and the amount of time between remeasurements can make attribution of C flux to specific events difficult. The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. The study was conducted in the forests of southwest Oregon State, USA, a region of diverse forest types, disturbances, and landowner management objectives. Plot data consisted of 676 NFI plots with remeasured individual tree data over a mean interval (time 1 to time 2) of 10.0 years. We calculated change in live aboveground woody carbon (AWC), including separate components of growth, mortality, and harvest. We interpreted radiometrically corrected annual Landsat images with the TimeSync (TS) tool for a 90 m × 90 m area over each plot. Spectral time series were divided into segments of similar trajectories and classified as disturbance, recovery, or stability segments, with type of disturbance identified. We calculated a variety of values and segment changes from tasseled cap angle and distance (TCA and TCD) as potential predictor variables of C flux. Multiple linear regression was used to model AWC and net change in AWC from the TS change metrics. The TS attribution of disturbance matched the plot measurements 89% of the time regarding whether fire or harvest had occurred or not. The primary disagreement was due to plots that had been partially cut, mostly in vigorous stands where the net change in AWC over the measurement was positive in spite of cutting. The plot-measured AWC at time 2 was 86.0 ± 78.7 Mg C ha−1 (mean and standard deviation), and the change in AWC across all plots was 3.5 ± 33 Mg C ha−1 year−1. The best model for AWC based solely on TS and other mapped variables had an R2 = 0.52 (RMSE = 54.6 Mg C ha−1); applying this model at two time periods to estimate net change in AWC resulted in an R2 = 0.25 (RMSE = 28.3 Mg ha−1) and a mean error of −5.4 Mg ha−1. The best model for AWC at time 2 using plot measurements at time 1 and TS variables had an R2 = 0.95 (RSME = 17.0 Mg ha−1). The model for net change in AWC using the same data was identical except that, because the variable being estimated was smaller in magnitude, the R2 = 0.73. All models performed better at estimating net change in AWC on TS-disturbed plots than on TS-undisturbed plots. The TS discrimination of disturbance between fire and harvest was an important variable in the models because the magnitude of spectral change from fire was greater for a given change in AWC. Regional models without plot-level predictors produced erroneous predictions of net change in AWC for some of the forest types. Our study suggests that, in spite of the simplicity of applying a single carbon model to multiple image dates, the approach can produce inaccurate estimates of C flux. Although models built with plot-level predictors are necessarily constrained to making predictions at plot locations, they show promise for providing accurate updates or back-calculations of C flux assessments. [ABSTRACT FROM AUTHOR]

Forest land managers rely on predictions of tree mortality generated from fire behavior models to identify stands for post-fire salvage and to design fuel reduction treatments that reduce mortality. A key challenge in improving the accuracy of these predictions is selecting appropriate wind and fuel moisture inputs. Our objective was to evaluate postfire mortality predictions using the Forest Vegetation Simulator Fire and Fuels Extension (FVS-FFE) to determine if using representative fire-weather data would improve prediction accuracy over two default weather scenarios. We used pre- and post-fire measurements from 342 stands on forest inventory plots, representing a wide range of vegetation types affected by wildfire in California, Oregon, and Washington. Our representative weather scenarios were created by using data from local weather stations for the time each stand was believed to have burned. The accuracy of predicted mortality (percent basal area) with different weather scenarios was evaluated for all stands, by forest type group, and by major tree species using mean error, mean absolute error (MAE), and root mean square error (RMSE). One of the representative weather scenarios, Mean Wind, had the lowest mean error (4%) in predicted mortality, but performed poorly in some forest types, which contributed to a relatively high RMSE of 48% across all stands. Driven in large part by over-prediction of modelled flame length on steeper slopes, the greatest over-prediction mortality errors arose in the scenarios with higher winds and lower fuel moisture. Our results also indicated that fuel moisture was a stronger influence on post-fire mortality than wind speed. Our results suggest that using representative weather can improve accuracy of mortality predictions when attempting to model over a wide range of forest types. Focusing simulations exclusively on extreme conditions, especially with regard to wind speed, may lead to over-prediction of tree mortality from fire. [ABSTRACT FROM AUTHOR]

The Oregon Department of Environmental Quality (ODEQ) uses Total Maximum Daily Load (TMDL) calculations, and the associated regulatory process, to manage harmful cyanobacterial blooms (CyanoHABs) attributable to non-point source (NPS) pollution. TMDLs are based on response (lagging) indicators (e.g., measurable quantities of NPS (nutrients: nitrogen {N} and phosphorus {P}), and/or sediment), and highlight the negative outcomes (symptoms) of impaired water quality. These response indicators belatedly address water quality issues, if the cause is impaired riparian functions. Riparian functions assist in decreasing the impacts of droughts and floods (through sequestration of nutrients and excess sediment), allow water to remain on the land surface, improve aquatic habitats, improve water quality, and provide a focus for monitoring and adaptive management. To manage water quality, the focus must be on the drivers (leading indicators) of the causative mechanisms, such as loss of ecological functions. Success in NPS pollution control, and maintaining healthy aquatic habitats, often depends on land management/land use approaches, which facilitate the natural recovery of stream and wetland riparian functions. Focusing on the drivers of ecosystem functions (e.g., vegetation, hydrology, soil, and landform), instead of individual mandated response indicators, using the proper functioning condition (PFC) approach, as a best management practice (BMP), in conjunction with other tools and management strategies, can lead to pro-active policies and approaches, which support positive change in an ecosystem or watershed, and in water quality improvement. [ABSTRACT FROM AUTHOR]

Urban heat is a growing environmental concern in cities around the world. The urban heat island effect, combined with warming effects of climate change, is likely to cause an increase in the frequency and intensity of extreme heat events. Alterations to the physical, built environment are a viable option for mitigating urban heat, yet few studies provide systematic guidance to practitioners for adapting diverse land uses. In this study, we examine the use of green infrastructure treatments to evaluate changes in ambient temperatures across diverse land uses in the city of Portland, Oregon. We apply ENVI-met® microclimate modeling at the city-block scale specifically to determine what built environment characteristics are most associated with high temperatures, and the extent to which different physical designs reduce ambient temperature. The analysis included six green infrastructure interventions modeled across six different land-use types, and indicated the varying degrees to which approaches are effective. Results were inconsistent across landscapes, and showed that one mitigation solution alone would not significantly reduce extreme heat. These results can be used to develop targeted, climate- and landscape-specific cooling interventions for different land uses, which can help to inform and refine current guidance to achieve urban climate adaptation goals. [ABSTRACT FROM AUTHOR]