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  • 1
    Online Resource
    Online Resource
    Simulations of snow distribution and hydrology in a mountain basin
    American Geophysical Union (AGU) ; 1999
    In:  Water Resources Research Vol. 35, No. 5 ( 1999-05), p. 1587-1603
    Details
    In: Water Resources Research, American Geophysical Union (AGU), Vol. 35, No. 5 ( 1999-05), p. 1587-1603
    Abstract: We applied a version of the Regional Hydro‐Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind‐driven sublimation to Loch Vale Watershed (LVWS), an alpine‐subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind‐driven sublimation was necessary to predict moisture losses.
    Type of Medium: Online Resource
    ISSN: 0043-1397 , 1944-7973
    URL: Article
    DOI: 10.1029/1998WR900096
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 1999
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  • 2
    Online Resource
    Online Resource
    Linaria dalmatica invades south-facing slopes and less grazed areas in grazing-tolerant mixed-grass prairie
    Springer Science and Business Media LLC ; 2012
    In:  Biological Invasions Vol. 14, No. 2 ( 2012-2), p. 395-404
    Details
    In: Biological Invasions, Springer Science and Business Media LLC, Vol. 14, No. 2 ( 2012-2), p. 395-404
    Type of Medium: Online Resource
    ISSN: 1387-3547 , 1573-1464
    URL: Article
    DOI: 10.1007/s10530-011-0085-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 2014991-8
    SSG: 12
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  • 3
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    Human and animal movements combine with snow to increase moose-vehicle collisions in winter
    IOP Publishing ; 2022
    In:  Environmental Research Letters Vol. 17, No. 12 ( 2022-12-01), p. 125007-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 17, No. 12 ( 2022-12-01), p. 125007-
    Abstract: Wildlife-vehicle collisions imperil humans, wildlife, and property. Collisions with moose (Alces alces ) are especially consequential and there are indications they may increase during severe winters. We tested hypotheses regarding the influence of moose movements and vehicular traffic patterns on collision risk. We first modeled daily snow depth and accumulation across 5.6 million km 2 of the North American Arctic-Boreal region. Next, we analyzed the movements and road use of 113 GPS-collared moose in response to snow depth. Finally, we examined the influence of these snow properties on vehicular traffic and 7680 moose-vehicle collisions. As winter progressed and the snowpack deepened in each study area, GPS-collared moose migrated to lower elevations, leading them into areas with shallower snow but higher road densities. This elevational migration corresponded with a higher probability of road-use by moose (by up to ten-fold) in winter than in summer. Corresponding to these patterns, moose-vehicle collisions were 2.4–5.7 times more frequent from December through February (compared to early summer). Collision risk was highest when and where snow depth was less than 120 cm, indicating that migration into areas with shallower snow increased collision risk in those areas. Most (82%) moose-vehicle collisions occurred after dark. This pattern was strongest during winter, when nighttime traffic volumes were eight times higher than summer due to longer nights. Overall, our findings suggest that concurrent seasonal changes in human and wildlife behavior increase the frequency of moose-vehicle collisions during winter. Snow depth influences collisions primarily through its impacts on moose movement, while strong seasonal changes in daylight hours cause an increase in nighttime traffic that further contributes to risk. This information may help predict times and places where risk of moose-vehicle collisions are highest and to develop seasonally dynamic mitigation strategies.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/aca8bf
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
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  • 4
    Online Resource
    Online Resource
    GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet
    Copernicus GmbH ; 2020
    In:  The Cryosphere Vol. 14, No. 11 ( 2020-11-11), p. 3935-3958
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 14, No. 11 ( 2020-11-11), p. 3935-3958
    Abstract: Abstract. Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    URL: Article
    DOI: 10.5194/tc-14-3935-2020
    DOI: 10.5194/tc-14-3935-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
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  • 5
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    Online Resource
    Arctic tundra shrub invasion and soot deposition: Consequences for spring snowmelt and near-surface air temperatures : SHRUBS, SOOT, SNOW, AND ARCTIC CLIMATE
    American Geophysical Union (AGU) ; 2007
    In:  Journal of Geophysical Research: Biogeosciences Vol. 112, No. G4 ( 2007-12), p. n/a-n/a
    Details
    In: Journal of Geophysical Research: Biogeosciences, American Geophysical Union (AGU), Vol. 112, No. G4 ( 2007-12), p. n/a-n/a
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/2006JG000297
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2007
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    detail.hit.zdb_id: 3094104-0
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    detail.hit.zdb_id: 3094268-8
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 3094181-7
    detail.hit.zdb_id: 3094219-6
    detail.hit.zdb_id: 3094167-2
    detail.hit.zdb_id: 2220777-6
    detail.hit.zdb_id: 3094197-0
    SSG: 16,13
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  • 6
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    Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)
    American Meteorological Society ; 2020
    In:  Journal of Hydrometeorology Vol. 21, No. 11 ( 2020-11), p. 2713-2733
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 21, No. 11 ( 2020-11), p. 2713-2733
    Abstract: 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-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the 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% decade −1 , peak SWE decreased by 14% decade −1 , and total snowmelt quantity decreased by 13% 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.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    URL: Article
    URL: Article
    DOI: 10.1175/JHM-D-20-0077.1
    DOI: 10.1175/JHM-D-20-0077.s1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
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  • 7
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    Modeling Snow Depth for Improved Simulation of Snow–Vegetation–Atmosphere Interactions
    American Meteorological Society ; 2004
    In:  Journal of Hydrometeorology Vol. 5, No. 5 ( 2004-10), p. 723-734
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 5, No. 5 ( 2004-10), p. 723-734
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    URL: Article
    DOI: 10.1175/1525-7541(2004)005〈0723:MSDFIS〉2.0.CO;2
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2004
    detail.hit.zdb_id: 2042176-X
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  • 8
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    Online Resource
    SnowSTAR2002 Transect Reconstruction Using a Multilayered Energy and Mass Balance Snow Model
    American Meteorological Society ; 2009
    In:  Journal of Hydrometeorology Vol. 10, No. 5 ( 2009-10-01), p. 1151-1167
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 10, No. 5 ( 2009-10-01), p. 1151-1167
    Abstract: The lateral and vertical variability of snow stratigraphy was investigated through the comparison of the measured profiles of snow density, temperature, and grain size obtained during the Snow Science Traverse—Alaska Region (SnowSTAR2002) 1200-km transect from Nome to Barrow with model reconstructions from the Snow Thermal Model (SNTHERM), a multilayered energy and mass balance snow model. Model profiles were simulated at the SnowSTAR2002 observation sites using the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) as meteorological forcing. ERA-40 precipitation was rescaled so that the total snow water equivalent (SWE) on the SnowSTAR2002 observation dates equaled the observed values. The mean absolute error (MAE) of measured and simulated snow properties shows that SNTHERM was able to produce good simulations for snowpack temperature but larger errors for grain size and density. A spatial similarity analysis using semivariograms of measured profiles shows that there is diverse lateral and vertical variability for snow properties along the SnowSTAR2002 transect resulting from differences in initial snow deposition, influenced by wind, vegetation, topography, and postdepositional mechanical and thermal metamorphism. The correlation length in snow density (42 km) is quite low, whereas it is slightly longer for snow grain size (125 km) and longer still for snow temperature (130 km). An important practical question that the observed and reconstructed profiles allow to be addressed is the implications of model errors in the observed snow properties for simulated microwave emissions signatures. The Microwave Emission Model for Layered Snowpacks (MEMLS) was used to simulate 19- and 37-GHz brightness temperatures. Comparison of SNTHERM–MEMLS and SnowSTAR2002–MEMLS brightness temperatures showed a very good match occurs at 19 GHz [a root-mean-square error (RMSE) of 1.5 K (8.7 K) for vertical (horizontal) polarization] and somewhat larger [5.9 K (6.2 K) for vertical (horizontal) polarization] at 37 GHz. These results imply that the simulation of snow microphysical profiles is a viable strategy for passive microwave satellite–based retrievals of SWE.
    Type of Medium: Online Resource
    ISSN: 1525-7541 , 1525-755X
    URL: Article
    DOI: 10.1175/2009JHM1098.1
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2009
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  • 9
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    Online Resource
    Predation risk across a dynamic landscape: effects of anthropogenic land use, natural landscape features, and prey distribution
    Springer Science and Business Media LLC ; 2018
    In:  Landscape Ecology Vol. 33, No. 1 ( 2018-1), p. 157-170
    Details
    In: Landscape Ecology, Springer Science and Business Media LLC, Vol. 33, No. 1 ( 2018-1), p. 157-170
    Type of Medium: Online Resource
    ISSN: 0921-2973 , 1572-9761
    URL: Article
    DOI: 10.1007/s10980-017-0590-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2016200-5
    SSG: 12
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  • 10
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    Simulating future climate change impacts on snow- and ice-related driving hazards in Arctic-boreal regions
    IOP Publishing ; 2023
    In:  Environmental Research Letters Vol. 18, No. 2 ( 2023-02-01), p. 025006-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 18, No. 2 ( 2023-02-01), p. 025006-
    Abstract: As Arctic and boreal regions rapidly warm, the frequency and seasonal timing of hazardous driving conditions on all-season Arctic-boreal roads are likely to change. Because these roads link remote Arctic areas to the rest of the North American road system, climate change may substantially affect safety and quality of life for northern residents and commercial enterprises. To gain insight into future hazardous driving conditions, we built Random Forest models that predict the occurrence of hazardous driving conditions by linking snow, ice, and weather simulated by a spatially explicit modeling system (SnowModel) to archived road condition reports from two highly trafficked all-season northern roads: the Dalton Highway (Alaska, USA) and Dempster Highway (Yukon, Canada). We applied these models to downscaled future climate trajectories for the study period of 2006–2100. We estimated future trends in the frequency and timing of icy, wet-icy, and snowy road surfaces, blowing and drifting snow, and high winds. We found that as the climate warms, and the portion of the year when snow and ice occur becomes shorter, overall frequency of snow storms and ice- and snow-related driving hazards decreased. For example, the mean number of days per year when roads are covered in snow or ice decreased by 51 d (−21%) on the Dalton Highway between the 2006–2020 and 2081–2100 time periods. However, the intensity of storms was predicted to increase, resulting in higher mean annual storm wind speeds (Dalton +0.56 m s −1 [+17%]) and snowfall totals (Dalton +0.3 cm [+36%]). Our models also predicted increasing frequency of wet-icy driving conditions during November, December, January, and February, when daylength is short and hazardous conditions may be more difficult to perceive. Our findings may help road managers and drivers adapt their expectations and behaviors to minimize accident risk on Arctic-boreal roads in the future.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/acb5b1
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2023
    detail.hit.zdb_id: 2255379-4
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