Search

Abstract. Given the tradeoffs between spatial and temporal resolution, questions aboutresolution optimality are fundamental to the study of global snow. Answersto these questions will inform future scientific priorities and missionspecifications. Heterogeneity of mountain snowpacks drives a need for dailysnow cover mapping at the slope scale (≤30 m) that is unmet for avariety of scientific users, ranging from hydrologists to the military towildlife biologists. But finer spatial resolution usually requires coarsertemporal or spectral resolution. Thus, no single sensor can meet all theseneeds. Recently, constellations of satellites and fusion techniques havemade noteworthy progress. The efficacy of two such recent advances isexamined: (1) a fused MODIS–Landsat product with daily 30 m spatialresolution and (2) a harmonized Landsat 8 and Sentinel 2A and B (HLS) product with3–4 d temporal and 30 m spatial resolution. State-of-the-art spectral unmixingtechniques are applied to surface reflectance products from 1 and 2 tocreate snow cover and albedo maps. Then an energy balance model was run toreconstruct snow water equivalent (SWE). For validation, lidar-basedAirborne Snow Observatory SWE estimates were used. Results show thatreconstructed SWE forced with 30 m resolution snow cover has lower bias, ameasure of basin-wide accuracy, than the baseline case using MODIS (463 mcell size) but greater mean absolute error, a measure of per-pixelaccuracy. However, the differences in errors may be within uncertaintiesfrom scaling artifacts, e.g., basin boundary delineation. Other explanationsare (1) the importance of daily acquisitions and (2) the limitations ofdownscaled forcings for reconstruction. Conclusions are as follows: (1) spectrallyunmixed snow cover and snow albedo from MODIS continue to provide accurateforcings for snow models and (2) finer spatial and temporal resolutionthrough sensor design, fusion techniques, and satellite constellations arethe future for Earth observations, but existing moderate-resolution sensorsstill offer value.

Abstract. Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤ 30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: 1) a fused MODIS - Landsat product with daily 30 m spatial resolution; and 2) a harmonized Landsat 8 - Sentinel 2A/B (HLS) product with 2–3 day temporal and 30 m spatial resolution. State-of-art spectral unmixing techniques are applied to surface reflectance products from 1 & 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size), but higher mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts e.g., basin boundary delineation. Other explanations are 1) the importance of daily acquisitions and 2) the limitations of downscaled forcings for reconstruction. Conclusions are: 1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models; and 2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations.

Abstract. Intrinsic albedo is the bihemispherical reflectance independent of effectsof topography or surface roughness. Conversely, the apparent albedo is thereflected radiation divided by the incident and may be affected bytopography or roughness. For snow, the surface is often rough, and these twooptical quantities have different uses: intrinsic albedo is used inscattering equations whereas apparent albedo should be used in energybalance models. Complementing numerous studies devoted to surface roughnessand its effect on snow reflectance, this work analyzes a time series ofintrinsic and apparent snow albedos over a season at a sub-alpine site usingan automated terrestrial laser scanner to map the snow surface topography.An updated albedo model accounts for shade, and in situ albedo measurementsfrom a field spectrometer are compared to those from a spacebornemultispectral sensor. A spectral unmixing approach using a shade endmember(to address the common problem of unknown surface topography) produces grainsize and impurity solutions; the modeled shade fraction is compared to theintrinsic and apparent albedo difference. As expected and consistent withother studies, the results show that intrinsic albedo is consistentlygreater than apparent albedo. Both albedos decrease rapidly as ablationhollows form during melt, combining effects of impurities on the surface andincreasing roughness. Intrinsic broadband albedos average 0.056 greater thanapparent albedos, with the difference being 0.052 in the near infrared or0.022 if the average (planar) topography is known and corrected. Fieldmeasurements of spectral surface reflectance confirm that multispectralsensors see the apparent albedo but lack the spectral resolution todistinguish between darkening from ablation hollows versus lowconcentrations of impurities. In contrast, measurements from the fieldspectrometer have sufficient resolution to discern darkening from the twosources. Based on these results, conclusions are as follows: (1) impurity estimatesfrom multispectral sensors are only reliable for relatively dirty snow withhigh snow fraction; (2) a shade endmember must be used in spectral mixturemodels, even for in situ spectroscopic measurements; and (3) snow albedomodels should produce apparent albedos by accounting for the shade fraction.The conclusion re-iterates that albedo is the most practical snowreflectance quantity for remote sensing.

Non-medical use (NMU) and diversion of prescription stimulants are prevalent on college campuses. Diversion represents a primary source of acquisition for NMU among young adults. This study examined relationships between stigmatizing beliefs related to NMU and diversion of stimulant medications and engagement in these behaviors, as well as how such perceptions are associated with indicators of psychological distress among those who engage in these behaviors. Young adults (N = 384) were recruited from a large US university to participate in this cross-sectional electronic survey-based study. Relationships between stigma variables and NMU and diversion were assessed. Among those who engage in NMU and diversion, we tested relationships between stigma variables and indicators of psychological distress, using validated instruments. Perceived social and personal stigmatic beliefs did not significantly predict NMU. However, perceived social and personal stigma of diversion significantly reduced diversion likelihood. For NMU, associations were found between stigma variables and indicators of psychological distress. Markedly, we found that as stigmatic perceptions of NMU increased, so did depressive, anxiolytic, and suicidal symptomatology among those who engage in NMU. Stigmatization does not deter NMU; however, stigmatization is positively associated with psychological harm among those who engage in NMU. Interventions should be developed to reduce stigmatization in order to improve psychological health among those who engage in NMU. Stigmatic perceptions of diversion were not predictive of psychological harm, though they are negatively associated with diversion behavior. [ABSTRACT FROM AUTHOR]

Background: Drug use is common and positively correlates with depression and suicidality. Drug users are highly stigmatized, as society associates shame, blame, incompetency, punishment, and criminality with these individuals. These societal views can become internalized and projected onto the self (i.e. self-stigma). Self-stigma is associated with affective dysregulation and maladaptive coping behaviors. To the best of our knowledge, the relationship between self-stigmatizing beliefs, depression, and suicidal ideation among student drug users has yet to be investigated. Methods: Participants of the current cross-sectional study were college students (n = 502) who reported past year illicit drug use. Students completed a questionnaire assessing demographics, self-stigmatizing beliefs, depression, and suicidal ideation. Correlations, multiple regression, and mediation modeling were used to test study hypotheses. Results: Roughly, half of the sample experienced suicidal ideation within the past year and 49% of participants met or exceeded an established threshold for major depression using validated instrumentation. Self-stigmatizing beliefs were a significant predictor of depression (p < 0.001) when adjusted for covariates. Furthermore, self-stigma significantly and positively associated with suicidal ideation (p < 0.001), when adjusted for depression and other covariates. Our modeling also supported a possible pathway, wherein, a significant indirect effect on suicidal ideation was observed for self-stigma through depression. Limitations: This study did not account for possible effects that frequency, recency, and type of drug may have had on variables of interest. Conclusions: Interventions reducing social stigma toward users and resulting self-stigma are needed in order to reduce depression and suicidality as well as encourage treatment-seeking.

Abstract. The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain CA USA is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the Western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand weighed to measure its water content. For this dataset, we present a clean and continuous hourly record of selected measurements from both sites covering the 2011–2017 water years which can be used to run a variety of snow models. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with Digital Object Identifier: https://doi.org/10.21424/r4159q.

Accurately estimating basin-wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50 m spatial resolution in several basins across the Western U.S. during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite-based snow estimates or models that can scale to whole mountain ranges, even those without ground-based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite-derived estimates of fractional snow-covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree-day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS.

Background: The changing cannabis legal climate in the United States has been associated with increased prevalence of use, more favorable general attitudes, and cultural norms toward its use among young adults. The purpose of this study was to conceptualize salient beliefs governing illicit cannabis use within a state where medical use is legal and adult use remains illegal. Methods: Following an initial qualitative pilot, a prospective study (baseline and a 30-day follow-up) that utilized an electronic survey design was conducted. Students at a large US university (N = 514) completed a baseline assessment of cannabis use and Theory of Planned Behavior (TPB) constructs, and 169 completed the follow-up assessing actual behavior. Results: Roughly 60% reported illicit cannabis use. Attitude and subjective norms accounted for 57% of the variation in intention for cannabis use in the next 30 days, while intention and perceived behavioral control (PBC) explained 51% of the variation in prospective use. The majority of salient beliefs identified exhibited moderate to strong correlations with intention and behavior among the larger sample. Close friends and partners were identified as important referents, outcome expectations of relaxation, improved social functioning, and more enjoyment, as well as environmental factors, such as proximity to use, safe utilization location, and continued illegality of adult use exhibited strong associations to intention and behavior. Conclusions: The practical utility of the salient belief measures identified herein presents a useful foundation for interventions aimed at reducing illicit cannabis utilization among young adults. [ABSTRACT FROM AUTHOR]

Accurate measurement and modeling of the snowpack energy balance are critical to understanding the terrestrial water cycle. Most of the water resources in the western US come from snowmelt, yet statistical runoff models that rely on the historical record are becoming less reliable because of a changing climate. For physically based snow melt models that do not depend on past conditions, ground based measurements of the energy balance components are imperative for verification. For this purpose, the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL) and the University of California, Santa Barbara (UCSB) established the “CUES” snow study site (CRREL/UCSB Energy Site, http://www.snow.ucsb.edu/) at 2940m elevation on Mammoth Mountain, California. We describe CUES, provide an overviewof research, share our experience with scientific measurements, and encourage future collaborative research. Snowmeasurements began near the current CUES site for ski area operations in 1969. In the 1970s, researchers began taking scientific measurements. Today, CUES benefits from year round gondola access and a fiber optic internet connection. Data loggers and computers automatically record and store over 100 measurements from more than 50 instruments each minute. CUES is one of only five high altitude mountain sites in the Western US where a full suite of energy balance components are measured. In addition to measuring snow on the ground at multiple locations, extensive radiometric and meteorological measurements are recorded. Some of the more novel measurements include scans by an automated terrestrial LiDAR, passive and active microwave imaging of snow stratigraphy, microscopic imaging of snow grains, snowflake imaging with a multi-angle camera, fluxes fromupward and downward looking radiometers, snowwater equivalent (SWE) from different types of snow pillows, snowmelt from lysimeters, and concentration of impurities in the snowpack. We give an example of terrain-corrected snow albedo measurements compared to several models and of sublimation measured from lysimeter and snow pillow melt. We conclude with some thoughts on the future of CUES.