Snow sublimation is an important component of the snow mass balance, but the spatial and temporal variability of this process is not well understood in mountain environments. This study combines a process‐based snow model (SnowModel) with eddy covariance (EC) measurements to investigate (1) the spatio‐temporal variability of simulated snow sublimation with respect to station observations, (2) the contribution of snow sublimation to the ablation of the snowpack, and (3) the sensitivity and response of snow sublimation to bark beetle‐induced forest mortality and climate warming across the north‐central Colorado Rocky Mountains. EC‐based observations of snow sublimation compared well with simulated snow sublimation at stations dominated by surface and canopy sublimation, but blowing snow sublimation in alpine areas was not well captured by the EC instrumentation. Water balance calculations provided an important validation of simulated sublimation at the watershed scale. Simulated snow sublimation across the study area was equivalent to 28% of winter precipitation on average, and the highest relative snow sublimation fluxes occurred during the lowest snow years. Snow sublimation from forested areas accounted for the majority of sublimation fluxes, highlighting the importance of canopy and sub‐canopy surface sublimation in this region. Simulations incorporating the effects of tree mortality due to bark‐beetle disturbance resulted in a 4% reduction in snow sublimation from forested areas. Snow sublimation rates corresponding to climate warming simulations remained unchanged or slightly increased, but total sublimation losses decreased by up to 6% because of a reduction in snow covered area and duration.

Abstract:Snow sublimation can be an important component of the snow-cover mass balance, and there is considerable interest in quantifying the role of this process within the water and energy balance of snow-covered regions. In recent years, robust eddy covariance (EC) instrumentation has been used to quantify snow sublimation over snow-covered surfaces in complex mountainous terrain. However, EC can be challenging for monitoring turbulent fluxes in snow-covered environments because of intensive data, power, and fetch requirements, and alternative methods of estimating snow sublimation are often relied upon. To evaluate the relative merits of methods for quantifying surface sublimation, fluxes calculated by the EC, Bowen ratio-energy balance (BR), bulk aerodynamic flux (BF), and aerodynamic profile (AP) methods and their associated uncertainty were compared at two forested openings in the Colorado Rocky Mountains. Biases between methods are evaluated over a range of environmental conditions, and limitations of each method are discussed. Mean surface sublimation rates from both sites ranged from 0.33 to 0.36 mm day À1 , 0.14 to 0.37 mm day À1 , 0.10 to 0.17 mm day À1 , and 0.03 to 0.10 mm day À1 for the EC, BR, BF and AP methods, respectively. The EC and/or BF methods are concluded to be superior for estimating surface sublimation in snowcovered forested openings. The surface sublimation rates quantified in this study are generally smaller in magnitude compared with previously published studies in this region and help to refine sublimation estimates for forested openings in the Colorado Rocky Mountains.

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km 2 ). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many timeconsuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

High‐resolution snow depth (SD) maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km2) in the Pyrenees were used to assess small‐scale variability of the snowpack at the catchment and sub‐grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49, and 99 × 99 m) and eight different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small‐scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small‐scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub‐grid variability. Topographic variables including slope, wind sheltering, sub‐grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small‐scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract:Water quality of the Big Thompson River in the Front Range of Colorado was studied for 2 years following a high-elevation wildfire that started in October 2012 and burned 15% of the watershed. A combination of fixed-interval sampling and continuous water-quality monitors was used to examine the timing and magnitude of water-quality changes caused by the wildfire. Prefire water quality was well characterized because the site has been monitored at least monthly since the early 2000s. Major ions and nitrate showed the largest changes in concentrations; major ion increases were greatest in the first postfire snowmelt period, but nitrate increases were greatest in the second snowmelt period. The delay in nitrate release until the second snowmelt season likely reflected a combination of factors including fire timing, hydrologic regime, and rates of nitrogen transformations. Despite the small size of the fire, annual yields of dissolved constituents from the watershed increased 20-52% in the first 2 years following the fire. Turbidity data from the continuous sensor indicated high-intensity summer rain storms had a much greater effect on sediment transport compared to snowmelt. High-frequency sensor data also revealed that weekly sampling missed the concentration peak during snowmelt and short-duration spikes during rain events, underscoring the challenge of characterizing postfire water-quality response with fixed-interval sampling.

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This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-year period (1984 – 2017). Atmospheric forcing was provided by the Phase 2 North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark-beetle and wildfire disturbances. Annual summary values of simulated snowpack properties (snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation) were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade-1, and downward trends in winter precipitation were -52 mm decade-1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20 percent decade-1, peak SWE decreased by 14 percent decade-1, and total snowmelt quantity decreased by 13 percent decade-1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

h i g h l i g h t sDeposition of NO 3 À and DIN were positively related to elevation.Summer DIN deposition was 25e50% greater on the east side of the park than on the west. A high-resolution geospatial model of summer DIN deposition was created for the park. Emissions sources and climate patterns affect spatial patterns in N and S deposition. Seasonal patterns in NO 3 À isotopes may reflect variations in emissions sources. a b s t r a c tLakes and streams in Class 1 wilderness areas in the western United States (U.S.) are at risk from atmospheric deposition of nitrogen (N) and sulfur (S), and protection of these resources is mandated under the Federal Clean Air Act and amendments. Assessment of critical loads, which are the maximum exposure to pollution an area can receive without adverse effects on sensitive ecosystems, requires accurate deposition estimates. However, deposition is difficult and expensive to measure in high-elevation wilderness, and spatial patterns in N and S deposition in these areas remain poorly quantified. In this study, ion-exchange resin (IER) collectors were used to measure dissolved inorganic N (DIN) and S deposition during June 2006eSeptember 2007 at approximately 20 alpine/subalpine sites spanning the Continental Divide in Rocky Mountain National Park. Results indicated good agreement between deposition estimated from IER collectors and commonly used wet þ dry methods during summer, but poor agreement during winter. Snowpack sampling was found to be a more accurate way of quantifying DIN and S deposition during winter. Summer DIN deposition was significantly greater on the east side of the park than on the west side (25e50%; p 0.03), consistent with transport of pollutants to the park from urban and agricultural areas to the east. Sources of atmospheric nitrate (NO 3 À ) were examined using N isotopes. The average d 15 N of NO 3 À from IER collectors was 3.5‰ higher during winter than during summer (p < 0.001), indicating a seasonal shift in the relative importance of regional NO x sources, such as coal combustion and vehicular sources of atmospheric NO 3 À . There were no significant differences in d 15 N of NO 3 À between east and west sides of the park during summer or winter (p ¼ 0.83), indicating that the two areas may have similar sources of atmospheric NO 3 À . Results from this study indicate that a combination of IER collectors and snowpack sampling can be used to characterize spatial variability in DIN and S deposition in high-elevation wilderness areas. These data can improve our ability to model critical loads by filling gaps in geographic coverage of deposition monitoring/modeling programs and thus may enable policy makers to better protect sensitive natural resources in Class 1 Wilderness areas.Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/3.0/).