Integrating snow science and wildlife ecology in Arctic

Environmental Research Letters

ACCEPTED MANUSCRIPT • OPEN ACCESS

Integrating snow science and wildlife ecology in Arctic-boreal North America To cite this article before publication: Natalie Boelman et al 2018 Environ. Res. Lett. in press https://doi.org/10.1088/1748-9326/aaeec1

Manuscript version: Accepted Manuscript Accepted Manuscript is “the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP Publishing of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP Publishing and/or its licensors” This Accepted Manuscript is © 2018 The Author(s). Published by IOP Publishing Ltd.

As the Version of Record of this article is going to be / has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately. Everyone is permitted to use all or part of the original content in this article, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by/3.0 Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record. View the article online for updates and enhancements.

This content was downloaded from IP address 139.81.71.12 on 07/11/2018 at 00:55

Page 1 of 29

pt

TITLE. Integrating snow science and wildlife ecology in Arctic-boreal North America AUTHORS. Natalie T. Boelman1, Glen E. Liston2, Eliezer Gurarie3,4, Arjan J. H. Meddens5,

us cri

Peter J. Mahoney4, Peter B. Kirchner6, Gil Bohrer7, Todd J. Brinkman8, Chris L. Cosgrove9, Jan U.H. Eitel5, Mark Hebblewhite10, John S. Kimball11, Scott LaPoint1,12, Anne W. Nolin9, Stine Højlund Pedersen2,13, Laura R. Prugh4, Adele K. Reinking2, Lee A. Vierling5 1

Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA 3 Department of Biology, University of Maryland, College Park, MD 20742, USA 4 School of Environmental and Forest Sciences, University of Washington, Seattle WA 98195, USA 5 Department of Natural Resources and Society, University of Idaho, Moscow, ID 83844, USA 6 Southwest Alaska Network, National Park Service, Anchorage, AK 99501, USA 7 Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA 8 Institute of Arctic Biology University of Alaska Fairbanks, Fairbanks, AK 99775, USA 9 Earth, Ocean and Atmospheric Sciences, Oregon State, Corvallis, OR 97331, USA 10 W. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA 11 Numerical Terradynamic Simulation Group, W. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT 59812, USA 12 Department of Migration and Immuno-ecology, Max-Planck Institute for Ornithology, Radolfzell 78315, Germany 13 Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK 99508, USA

an

2

pte

dM

ABSTRACT. Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover’s spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fit-for-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA’s ABoVE and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties.

ce

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

KEYWORDS. ABoVE; Arctic boreal vulnerability experiment; boreal; caribou; Dall sheep; polar bear; remote sensing; snow; wildlife.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

AUTHOR SUBMITTED MANUSCRIPT - ERL-105606.R1

1


I.

INTRODUCTION

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover’s spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack (Brown and Mote 2009; Callaghan et al. 2011; Winski et al. 2017). To date, a small amount of research has highlighted the consequences of these changing snowscape properties on the biogeochemistry, hydrology, and energy balance of ABRs (Callaghan et al. 2011), while far fewer have focused on the effects of shifting snowscape dynamics on wildlife. Despite their specific adaptations to snow, in many cases, these species rely heavily on favorable snow conditions for their fitness (survival and reproductive success). Since ABRs host some of the last remaining pristine regions of the planet with intact wildlife communities - and are home to such spectacles as the longest overland migration in the world (i.e. caribou) - understanding the effects of changing snowscapes on its wildlife communities is critical given that they are experiencing pronounced alterations due to shifting temperature and precipitation regimes associated with global climate change. There is growing appreciation of the effects of snowscape properties on wildlife within the ABR, yet snow data available to assess these effects often have insufficient spatial and temporal resolutions and extents. Furthermore, many of the specific physical properties of snowscapes most relevant to wildlife ecology in ABRs remain unquantified over large spatial and temporal scales. Current ABR-wide datasets are limited to what can be estimated from established space-borne sensors or models for climate and hydrologic applications; they include snow variables such as snow-covered area, snow-cover duration, snow albedo, snow-water equivalent (SWE), and snow surface temperature and freeze-thaw state. Other snowscape properties such as depth, density, stratigraphy, ice layers, and snow-layer hardness are significant factors affecting wildlife (Laperriere and Lent 1977; Skogland 1978; Collins and Smith 1991; Forchhammer and Boertmann 1993); yet observations of these variables are sparse and ABRwide datasets do not exist. Furthermore, ABR wildlife biologists are frequently confronted with a lack of available snow products at suitable spatial and temporal scales, and this a major limiting factor in their research efforts. Therefore, our ability to understand, predict, and respond to snowwildlife interactions is limited by the inadequate spatial snow products that are currently available. Given the rapid rate at which climate change is altering ABR snowscapes, the goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Our objectives are to:

ce

pte

dM

an

us cri

pt

49

•

highlight the proven importance of snowscape dynamics to wildlife ecology as demonstrated by previous studies conducted in snow dominated ecosystems, including ABRs (Section II)

•

summarize and describe the snow-related spatial products currently available for studying snowscape dynamics in ABRs of Alaska and northwestern Canada (Section III)

•

demonstrate that new and improved snow spatial products are required to reveal, understand, and quantify snow-wildlife interactions in ABRs (Section IV)

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 29

2

Page 3 of 29

pt

II.

provide a future prospectus for how to create fit-for-purpose snow spatial products for understanding wildlife responses to the changes in snowscape properties that are expected to continue as climate continues to warm in ABRs (Section V)

THE IMPORTANCE OF SNOWSCAPES TO WILDLIFE

us cri

•

pte

dM

an

In snow-dominated ecosystems, the amount, distribution and physical characteristics of snow have significant impacts on wildlife populations (Formozov 1946). Snowscape properties can either negatively or positively influence wildlife movements (e.g. Pruitt 1959), predator-prey dynamics (e.g. Sokolov et al. 2016), access to food (e.g. Fancy and White 1985), thermal insulation (e.g. Kausrud et al 2008), and, ultimately, their reproductive success and survival (as reviewed in Berteaux et al. 2017). While most species wade through the snowpack, some species require the insulation provided by the subnivean environment to den, burrow, move, forage, take cover from predators, and ultimately survive and reproduce. As demonstrated in the paragraphs below, a broad suite of snow properties must be explicitly considered in order to understand how ABR wildlife are being affected by and responding to rapid, ongoing changes in snowscape conditions caused by climate warming. Variability in snow cover duration - defined as the period between snow cover onset in the fall and the snowpack disappearance date in the spring or early summer - has important implications for a wide range of wildlife species. Several studies have found that migratory shorebirds and passerines breeding in the Arctic alter the timing of egg-laying in years with early or late snowmelt dates (Meltofte et al. 2007; Smith et al. 2010; Grabowski et al. 2013; Liebezeit et al. 2014; Boelman et al. 2017). Since the timing of clutch initiation can affect clutch size, opportunity for re-nesting, timing of autumnal migration, and the size and skill of young of the year by the onset of winter conditions, snow disappearance dates can influence reproductive success in these birds (Meltofte 1985, 2000; Nol et al. 1997; Sandercock et al. 1999). The timing of spring and fall snow cover also directly influences predator-prey dynamics. For example, in years with early snow disappearance dates, rodents that overwinter in the subnivean environment (such as voles and lemmings, Arvicolinae) are exposed to avian predators earlier in the season relative to years with later snow disappearance (Duchesne et al. 2011; Bilodeau et al. 2013; Berteaux et al. 2016). In early snowmelt years, snowshoe hares (Lepus americanus) show little phenotypic plasticity in the phenology of their spring coat color change from white to brown (Zimova et al. 2014). As such, models predict that as snow cover duration continues to decrease with climate warming, the seasonal mismatch between their persistently white coat color and the increasingly snow-free landscape will strengthen. The diminishment of their seasonal camouflage will markedly increase the susceptibility of the hares to predation (Mills et al. 2013). In fact, similar seasonal mismatches between animal coat and landscape color phenologies are predicted to develop in several other Arctic-boreal species as the snow-covered season continues to shorten (Mills et al. 2013; Henden et al. 2017). In summary, migration patterns, predator-prey relationships, and physical adaptations to snowcovered landscapes are strongly influenced by the timing and duration of the snow season, and thus changes in snowscape seasonality will lead to cascading effects on a wide range of animal species. In addition to snow-cover duration, a suite of other physical snowpack characteristics is critical to wildlife movement, habitat selection, survival, and reproduction. Multiple studies have shown that deep, soft snow, as well as wet snow, impede animal movements and increase energy

ce

96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3


pte

dM

an

us cri

pt

expenditure demands (Fancy and White 1987; Nicholson et al. 2016), potentially affecting demographic rates (Parker et al. 2009). Body mass, limb length, and foot loading (body weight divided by total foot area contacting snow) strongly influence the effect of snow depth on locomotion energetics (Kelsall and Telfer 1971; Kelsall and Prescott 1971; Mech et al. 1971; Telfer and Kelsall 1984). For this reason, northern ungulates (hooved mammals) tend to select areas of shallow snow to overwinter (Stelfox and Taber 1968; Duquette 1988). Le Corre et al. (2017) reported that spring caribou migration was earlier following mild winters, and was later when April snowfall was high, suggesting that the animals may adjust migration timing in response to more favorable snow conditions along their migration routes. Large ungulates, such as elk (Cervus canadensis), moose (Alces alces), mountain goats (Oreamnos americanus), and bighorn sheep (Ovis canadensis), tend to avoid deep snow that they sink in easily (Parker et al. 1984; Dailey and Hobbs 1989). Species-specific responses to variations in snowscape properties can also play a significant role in shaping predator-prey interactions during the snow season. Rates of predation on deer and moose are highest when snow is deepest (Mech and Frenzel 1971; Peterson and Allen 1974; Haber 1977; Lendrum et al. 2017), largely because ungulates have a higher foot load than wolves, making them slower in deep, soft snow, and thus less able to escape wolf predation (Nelson and Mech 1986). Accessibility of ground forage is also influenced by the physical snowpack properties. For example, caribou, reindeer, and sheep forage where snow is shallowest and least dense (Hoefs and McTaggart-Cowan 1980; Fancy and White 1985; Duquette 1988; Nichels and Bunnell 1999; Johnson et al. 2001; Beumer 2017). When and where snow depth, density, hardness, and ice layers limit access to ground lichen, woodland caribou shift to feeding on arboreal lichen (Johnson et al. 2001). More generally, the increasing occurrence of rain and snowmelt freeze events - which create hard ice layers on, within, or beneath the snowpack inhibit access to forage, causing catastrophic die-offs in northern ungulate populations (Putkonen et al. 2009; Rennert et al. 2009; Stien et al. 2010, 2012; Hansen et al. 2011, 2013). These snowpack-induced die-off events can have cascading effects that ripple through the food web. Sokolov et al. (2016) documented such an event that created an abundance of ungulate carcasses which produced a resource pulse the following summer, thereby supporting an abundance of red fox (Vulpes vulpes) and hooded crows (Corvus cornix), which in turn increased predation pressure on rodents and ground-nesting bird species the following spring and summer. In addition, species of ptarmigan (Lagopus) - the smallest bird in the grouse family - migrate across ABRs and are active throughout winter, browsing on the buds of shrubs such as willow and birch that protrude from the snowpack. They depend on snow for their burrows, but deep snowpacks may inhibit their browsing access to shrubs. Their browsing habits affect shrub architecture and height, thereby creating a feedback between browsing and snow accumulation (Liston et al. 2002; Tape et al. 2010). The quality of overwintering habitat for species that den, hibernate, or remain active in the subnivean environment is generally highest in deep, low density snow because the insulative capacity of snow increases with depth and decreases with density (Pomeroy and Brun 2001). For this reason, studies conducted in Yellowstone National Park show that grizzly bears (Ursus arctos) tend to choose hibernation den sites on slopes where prevailing winds cause deep snow to accumulate, insulating dens from outside frigid air temperatures as low as -45°C (Craighead and Craighead 1972; Vroom et al. 1980). Several small mammal species are able to remain active within the subnivean layer throughout the winter due to these insulative properties. In a field study conducted at three Canadian Arctic sites, Reid et al. (2012) found that lemmings used

ce

143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 29

4

Page 5 of 29

III.

us cri

pt

areas with deeper snow (i.e., more thermal insulation) more frequently than areas with shallower snow (i.e. less insulation). As the frequency of winter rain and snowmelt freeze events continue to increase in the Arctic due to changing climate regimes (Kim et al. 2015), resulting increases in snow density, hardness, and presence of ice layers will reduce the snowpack’s insulating properties on which these species depend (Kausrud et al 2008; Berteaux et al. 2017; Penczykowski et al. 2017). Due to their numerical dominance and central position in ABR food webs, dynamics in the survival and reproductive success of voles and lemmings affects numerous plant and predator species by altering trophic interactions (Ims and Fuglei 2005).

CURRENT SPATIAL SNOWSCAPE PRODUCTS FOR ABRs

an

Scientists quantify and monitor variations and trends in snowscape properties using insitu measurements, remotely-sensed observations, numerical modeling, and by integrating these approaches through model-data assimilation and reanalysis products. We have listed and summarized the attributes of the spatial snow products that are - to the best of our knowledge currently available for the ABRs of North America (Table S1). These products were produced for a number of purposes, ranging from operational earth observations to studies of ecosystem processes at various scales. While these data are used when and where they are available, they 207 are often inadequate for wildlife studies. The 208 three sub-sections that follow summarize the 209 types of ABR snow-related products that are 210 available for wildlife studies. 211 212 In-situ measurements of snowscape 213 properties. The ABR is a vast and 214 geographically diverse region with sparsely 215 distributed in situ observations of snow and 216 other weather-related station observations; 217 the spatial distribution of these 218 measurements are generally inadequate to 219 accurately represent environmental gradients 220 that influence snow such as altitude, latitude, 221 and the distance from the coast. During the 222 snow cover season, the temporal resolution 223 of in-situ snow measurements in ABRs are 224 typically hourly at automated meteorology Figure 1. Total number (brown) of active 225 and snow measurement stations and monthly SNOTEL stations 1910-2017 and mean Feb. 226 at manual snow course sites. These 2 snow covered area per 10,000 km (blue) for measurements typically lack the spatial Washington, Oregon, California, Arizona,227 228 coverage required to quantify variability in Nevada, Utah, Idaho, Montana, Wyoming, 229 snowscape properties across the range of and New Mexico combined (solid) and Alaska (dashed). 230 spatial scales relevant to wildlife behavior. 231 While interpolated data from reanalysis or physical and statistical models are widely used, the utility of such data in wildlife studies remains limited by the sparse in situ observations available for calibration and validation.

ce

pte

dM

189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


232 233

5


dM

an

us cri

pt

Relative to the snow cover of the western conterminous United States, which has < 50% snow covered area for less than 6 months per year, Alaska has > 95% snow covered for 7 to 10 months of per year (Liston and Hiemstra 2011, Robinson, Estilow 2018). When compared to the Western states of the conterminous United States, Alaska has < 1/8 the density of in-situ snowcover observations. Figure 1 highlights this, showing the number and density of active Natural Resource Conservation Service (NRCS) snow measurement sites in the combined area of Washington, Oregon, California, Arizona, Nevada, Utah, Idaho, Montana, Wyoming, and New Mexico (mean February snow covered area = 1.31 x 106 km2) compared with Alaska (mean February snow covered area = 1.72 x 106 km2) (NRCS 2017 wcc.nrcs.usda.gov/snow/, US Census Bureau 2012). The limited periods of record and sparse spatial distribution of in situ data in Alaska further constrain calibration and validation of both remote-sensing observations and spatial models of snowscape properties (Bokhorst et al. 2016). While many of the snow observing sites in the ABR measure SWE, snow depth, air temperature, and total precipitation, other variables critical to understanding wildlife behavior are rarely measured, including snow hardness and ice-layers, and local scale spatial heterogeneity in these and the other snow properties. In addition to readily accessible snow data, other public and private stakeholders (e.g. United States Department of Agriculture Forest Service, United States National Park Service, petroleum and mining companies) collect limited in situ snow observations in Alaska. These data are also sparsely distributed and may be reported using different formats or, in the case of private industry, unavailable to the public, making them more difficult to discover, access, and utilize. In recognition of the need for coordinated management and archiving of ABR data, some notable efforts have emerged to locate and consolidate these data such as the NSF Arctic Data Center (arcticdata.io/) and NOAA’s Sustaining Arctic Observing networks (arcticobserving.org/).

pte

Remotely sensed retrievals of snowscape properties. The use of satellite data for global mapping of snow-cover extends over the modern satellite era. However, it was not until the launch of the daily-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the NASA EOS Terra (1999) and Aqua (2002) satellites, and more recently the reanalysis of satellite passive microwave sensor records, that daily snow products describing the extent, albedo, grain size, and relative ice and water content have become available (Tedesco, 2013). The spatially continuous nature of satellite information complements in situ observations that are spatially limited. Unfortunately, effective remote-sensing techniques to map and monitor snow properties at the spatial and temporal resolutions required to understand wildlife behavior are still lacking (Nolin, 2010; Dietz et al. 2012; Bokhorst et al. 2016). As shown in Section IV, the spatial resolution of snow-related information required to characterize wildlife behavior can range from meters (e.g. polar bear (Ursus maritimus) denning habitat) to kilometers (e.g. caribou migration timing). Similarly, regular daily observations that capture transient snowscape conditions and anomalous events are also required (e.g. snow-free date and water or ice content of the snowpack). Another major challenge for remotely derived snow assessments is that wildlife respond to a broad range of ecosystem-specific snowscape properties that are not available from current remote-sensing observations and data products. Existing satellite-derived snow products meet some snow-wildlife study requirements (Table S1), but no single sensor or data record provides consistent and reliable observations of all of the relevant snow variables at the required spatial and temporal scales. Most available satellite records are limited by one or more factors, including the trade-off between spatial

ce

234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 29

6

Page 7 of 29

an

us cri

pt

resolution and temporal frequency; where more frequent sensor scans (e.g. Passive Microwave and MODIS) have coarser footprints when compared to those scanning smaller areas that can resolve information at finer spatial resolution (e.g. Landsat) but are only available at much coarser temporal resolution (e.g. 16 days). Each sensor type also has limitations causing other data gaps and uncertainties. Optical and infrared sensors are limited by low solar illumination, polar night, and persistent cloud cover. The signal strength of microwave energy emitted from Earth’s surface, complex terrain, vegetation cover, and snow-cover properties (e.g. stratigraphy, grain size) likewise complicate data retrieval by microwave sensors (Nolin 2010; Dietz et al. 2012). Airborne or satellite image retrievals are also used to create high resolution digital elevation or surface elevation models (DEM and SEM, respectively) that are in turn input into physical snowpack evolution models to study snow-wildlife interactions (see Model simulations of snowscape properties). Unfortunately, much of the digital elevation and surface mapping of the ABR is of relatively low spatial resolution, with two notable exceptions: the Alaska Interferometric Synthetic Aperture Radar (IFSAR) DEM (5-m resolution), and the SEM called the ArcticDEM, created using stereo pairs of high resolution optical imagery (2-m resolution) (LPDAAC, 2017, Polar Spatial Center, 2017). Higher resolution airborne LiDAR and structure from motion products are also available in a few locations (as described in case study 1) and provide the opportunity to conduct wildlife relevant snowscape research by mapping snow depth through differencing (Deems et. al., 2013; Nolan et. al. 2015).

pte

dM

Model simulations of snowscape properties. Temporally varying snowpack properties can be extrapolated over landscapes using process-based models that implement snow physics within the context of a numerical modeling system (e.g. Durand et al. 1999; Bartelt and Lehning 2000; Lehning et al. 2006; Liston and Elder 2006a). These models input time-invariant variables such as topography and land-cover, and time-evolving meteorological forcing such as air temperature, humidity, precipitation, wind, and radiation, and simulate the evolution of variables such as SWE, snow albedo, and snow density. Existing snow-evolution models faithfully represent key snow-related processes; including snow-water-equivalent accumulation of snowfall, redistribution by wind, snow-forest-canopy interactions, snow-vegetation interactions, and snowmelt. In addition, these snow modeling tools appropriately handle the spatial-distribution and temporal-evolution aspects of snowpack growth and decay. Often these models have been developed for a specific application (e.g. climate studies, water resource management, avalanche risk assessment), and the details of the model configuration (e.g. grid increment, time step, and represented physical processes) reflect that focus. To date, because the focus has rarely been on wildlife applications, model configurations have rarely met the needed criteria to produce snow products that are directly applicable to understanding wildlife-snow interactions (Liston et al. 2007). Modeling approaches for characterizing snowscape properties can be developed to produce a large suite of wildlife-relevant snow variables over large spatial and temporal extents, and over a wide range of spatial and temporal resolutions. In this way, modeled snowscape products can be tailored to fit the specific needs of wildlife studies (e.g. Vikhamar-Schuler et al. 2013; Eaton and Businger 2014; Northrup et al. 2016; Ouellet et al. 2016; Rasmus et al. 2016; Lendrum et al. 2017; Reinking et al. 2017). While current snow-evolution models can make valuable contributions to snow-wildlife interaction studies, the majority are not yet up to the task

ce

280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


7


CASE STUDIES: THE IMPORTANCE OF WILDLIFE-RELEVANT SNOWSCAPE PRODUCTS AT APPROPRIATE ANALYSIS SCALES

dM

IV.

an

us cri

pt

because they do not simulate the required snow variables that are often most critical to wildlife dynamics studies, although there are some notable exceptions (see Table S1). Other weaknesses associated with snow-evolution models limit their ability to simulate or estimate snowscape properties, which in turn limits their efficacy in studies of wildlife-snow interactions. For example, the models are only as good as the meteorological forcing inputs; these data can suffer from deficiencies in data quality or resolution. Currently the most problematic meteorological input is the precipitation forcing; the available data on waterequivalent precipitation (i.e. total precipitation of various forms falling from the sky and reaching the ground) are often of insufficient quality to exactly reproduce observed snow distributions (Goodison et al. 1981; Yang et al. 1998; Liston and Sturm 2002; Pan et al. 2003; Liston et al. 2008, 2016; Girotto et al. 2014; Margulis et al. 2015; Wrzesien et al. 2017). Often times there are no validation data to compare the model outputs to, and available topography and vegetation datasets may be inadequate. In addition, it has been challenging to develop snow models that are general enough to consistently and accurately simulate snow properties throughout the entire ABR for the entire scope of processes found within the domain. This is because of the wide range of processes found in warm-wet, cold-dry, tundra, and forested environments. The existing models almost always produce qualitatively reasonable snowproperty distributions when driven with realistic forcing data. However, their outputs may include biases when compared to in-situ observations of snow properties made at a given location and time.

This section presents three case studies that demonstrate the need for new, wildliferelevant snow variables, at spatial and temporal scales that are generally not available in existing spatial snow products. These examples highlight our use of specialized snowscape products to understand current snow-wildlife interactions and how those interactions may change in the future.

pte

Case study 1: Mapping polar bear den habitat requires fine-scale snow depth estimates. Pregnant polar bears typically den in snowdrifts found on sea ice or land (Amstrup 2003). As sea ice has become progressively thinner and unstable (Comiso et al. 2008; Kwok and Rothrock 2009; Maslanik et a. 2011; Polyakov et al. 2012), the proportion of land-based dens has increased from 42% (in the 1980s) (Amstrup and Gardner 1994) to 63% (in the early 2000s) (Fischbach et al. 2007). Coupled with concurrent increases in human activity in the coastal regions of Alaska’s Beaufort Sea, the potential for human-bear interactions has heightened. To minimize interactions, as well as disturbance to denning bears, it is critical to identify active dens (MacGillivray et al. 2003; Stirling and Derocher 2012). As such, Liston et al. (2016) developed, applied, and tested a physically based numerical model (SnowDens-3D) to map land and barrierisland polar bear snowdrift den habitat along the Beaufort Sea coast. Liston et al. (2016) found that snowdrift dens are not randomly distributed, but are the direct result of wind blowing snow into topographic snowdrift traps. They used SnowDens-3D to simulate the year-specific physical interactions of concurrent snow, wind, and topographic dynamics. The model was run annually from 1995 through 2012, on a 2.5 m by 2.5 m horizontal grid; a grid sufficient to resolve the snowdrifts that polar bears den in. The minimum snow-depth

ce

325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 29

8

Page 9 of 29

pt

required for a viable den was defined and applied to the SnowDens-3D simulated snow depths. This yielded annual maps of potential polar bear den habitat locations, which change every year depending on wind speed and direction, and snowfall amounts. They found that 97% of observed maternal den locations were correctly identified in the simulated snowdrifts modeled by SnowDens-3D (Fig. 2). The model’s high level of accuracy is due to its use of high resolution (2.5 m) topographic data and its ability to account for the influences of year-specific snowfall and wind speed and direction. Given that the existing suite of widely available spatial snow products does not include snow depth data with comparable accuracy nor spatial resolution (see Table S1), this case study illustrates the clear need for a highly specialized snow product for identifying polar bear denning habitat, and provides an example of a wildlife-relevant snowrelated variable that is not available by field or remote sensing methods alone. Because of SnowDens-3D's basis in snow and weather physics, it can be incorporated into climate scenario 383 simulations to explore how shifts in early-winter 384 meteorological conditions are likely to impact the 385 formation and availability of polar bear snowdrift 386 den habitat.

us cri

371 372 373 374 375 376 377 378 379 380 381 382

dM

an

Figure 2. Polar bear den habitat locations simulated with SnowDens-3D over Cottle Island on Alaska’s Arctic coast using a 2.5 m grid (Liston et al. 2016). Snow depths over 1.5 m are displayed in red and the snowdrift edges have been widened by 12.5 m to improve visibility. Polar bear dens, which change locations each year, were observed at the centers of the large open circles during from 1995 through 2012. Blue is ocean, white is land.

387

Case study 2: Understanding Dall sheep movement behavior requires fineand course-resolution snowscape products. The Dall sheep (Ovis dalli dalli) is an

pte

iconic northern wildlife species endemic to the mountain ranges of Alaska and northwestern Canada. Range-wide, Dall sheep populations have declined by at least 20% during the past decade, with some areas declining up to 70% and causing emergency harvest closures (Koizumi et al. 2011, Alaska Department of Fish and Game 2014). Dall sheep may be particularly sensitive to changing snow conditions, because sheep occur at both high elevations and high latitudes, and average temperature increases and the associated changes are likely amplified due to reductions in the cold regions’ temperature inversions (Callaghan et al. 2011). Dall sheep exhibit seasonal movements that may be tied to seasonal changes in snow conditions, including use of wind-exposed patches of forage along ridgelines during peak snow coverage and short-distance elevational migrations (Simmons 1982; Nichols and Bunnell 1999). Mahoney et al. (2018) evaluated the efficacy of MODIS snow-cover fraction and SnowModel (Liston and Elder 2006a) snow depth and density products in predicting Dall sheep movements at multiple spatial and temporal scales, finding that adding a snow covariate, regardless of type, substantially improved model predictions of sheep movements (Fig. 3a). However, the relative performances of snow products were scale-dependent. At higher resolutions (25 & 500-m), SnowModel products that included blowing-snow redistribution, snow-density evolution,

ce

388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


9


us cri

pt

orographic precipitation increases, and slope-aspect snowmelt relationships, outperformed MODIS at predicting the finest movement scales (i.e. the distances traveled over 7-56 hours). However, the 500-m MODIS fractional snow cover outperformed SnowModel’s gridaveraged snow products at coarser resolutions and movement scales (>112 hours), likely because the coarser resolution SnowModel outputs did not include the sub-grid snow information reflected in the MODIS data (Fig. 3a). At fine movement scales (i.e., 7 hours), Dall sheep generally selected low density, shallow snow (Fig. 3b,c), likely to facilitate access to forage and Figure 3. The relative fit of models (quantified using the QIC (Quasilikelihood under the Independence model Criterion)) with MODIS fractional snow cover (grey circles), SnowModel snow depth at 25-m (small white circles) and 500-m (large white circles), and no snow metrics (black circles) (a). The bestfitting model of fine-scale (7 hr time step only) resource selection in sheep predicted non-linear responses to snow density and depth (b,c). This best fitting snow model was used to produce a heat map of predicted space use for 1 April 2006 in Lake Clark National Park (d; darker reds equate to higher probability of use). Included with the map is an overlay of observed sheep locations (black circles) from 26 March - 4 April 2006 as a visual representation of model performance. Adapted from Mahoney et al. 2018.

pte

reduce energy expenditure (Dailey and Hobbs 1989). However, they selected for higher snow density at or above the mean threshold of support (329 kg·m-3, SE = 18; Sivy et al. in press) when traveling through deep snow above mean chest height (54cm; Fig. 3b,c), likely to reduce snow hoof penetration and improve efficiency of movement (Parker et al. 1984). At coarse scales, Dall sheep selected for areas with lower fractional snow cover (Fig. 3d), indicating sheep may be detrimentally affected if regional trends of increased winter precipitation continue (Olsen et al. 2011; Winski et al. 2017). These scale-dependent differences in snow product performance indicate that use of a snowpack evolution model such as SnowModel may be necessary to obtain critical insights regarding fine-scale responses of animals to changing snow properties, whereas MODIS fractional snow cover data may be able to provide insight into the effects of snow on movements at broader scales. Although MODIS products are readily available and relatively easy to use, incorporating snow cover in this case required removing a substantial proportion of animal

ce

414 415 416 417 418 419 420 421 422 423 424 425 426 427 428

dM

an

407 408 409 410 411 412 413

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 29

10

Page 11 of 29

pt

location data due to cloud cover inhibiting image acquisition (82.1% of used locations removed), which reduced the statistical power and the ability to characterize complex movements in response to fine-scale spatial processes.

Case study 3: Caribou migrate well in advance of snow cover melt. During their

dM

an

us cri

spring migrations to calving grounds, barren-ground caribou (Rangifer tarandus spp.) travel up to one thousand kilometers within only weeks. There is considerable variability in the timing of the spring migrations across years, but relatively few large-scale studies have explored the environmental drivers of these variations (though see LeCorre 2017). The amount of spring snow is even more variable across years, but has shown a steady negative trend in high northern latitudes for several decades (Derksen and Brown 2012). In temperate zones, snowmelt precipitates spring green-up, which has been shown to be an important driver of ungulate migrations (Aikens et al. 2017). It therefore seems reasonable to hypothesize that the timing of snowmelt might drive spring migration phenology of caribou, as well. To explore these relationships, we analyzed caribou movement data collected during 15 consecutive spring migrations (2000 - 2014) from the Bathurst herd in central Canada that has historically been one of the largest migratory herds in the world. To quantify the timing of snowmelt, we computed snow disappearance day (SDD) for each 500-m MODIS pixel, averaging neighbors in space and time to fill gaps. Caribou generally started migrating more than three weeks prior to snowmelt, with high variability among years (mean DAS = 21.9 days, standard deviation 17.5), and arrived at the calving grounds one week before snowmelt (mean 7.3 days, s.d. 12). Spring migration for collared caribou in 2011 overlaid on MODIS snow-cover data (MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6 - Table S1, item 19) is presented in Figure 4. The 2011 migration began well before snowmelt and continued until well ahead of the melting snow front, which was the case in all years of the migration. We 455 investigated whether the 456 inter-annual variation in 457 migration timing was 458 explained by timing of 459 snowmelt, as indexed by the 460 median SDD in the entirety of 461 the wintering and calving 462 ranges, respectively. These 463 exploratory analyses indicate 464 that caribou do not migrate in 465 a consistent way with respect 466 to MODIS-derived snowmelt 467 timing. While caribou tended

ce

pte

429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454

Figure 4. Snapshots of Bathurst caribou movements (circles) over a 468 two-month period in 2011 encompassing the spring migration. 469 Background colors represent MODIS snow cover (pink-light blue470 white) and NDVI (brown-green tones). The pink circles illustrate the 471 missing data (e.g. all of June 4 and most of June 11) obscured by 472 cloud cover. 473

to move ahead of the snowmelt front, they were as likely to move anywhere from one day to three weeks ahead of the retreating snow front. Our findings suggest that if snow-related drivers are important to the timing of migration, snow properties other than cover -

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


474

11


V.

us cri

pt

the most readily accessible of remotely sensed snow data – are more important. For example, local snow quality, including snow variables such as depth, hardness, and finer-scaled spatial patterns of snow cover and timing of ice cover melt on the tundra’s many waterbodies, may be more important. To more fully explain the spring phenology of caribou migration a combination of more sophisticated snow products and more ecologically driven combinations of climatic variables (e.g. temperature and precipitation to assess snow quality) need to be taken into account.

A FUTURE PROSPECTUS FOR IMPROVING WILDLIFESPECIFIC SNOWSCAPE PRODUCTS FOR ABRs

dM

an

The snowscape-related questions being asked by the wildlife community span a wide range of spatial and temporal scales and a comprehensive suite of wildlife-relevant snow variables. As demonstrated in the case studies above, spatial and temporal resolution requirements range from meters to kilometers, and from hours to decades. The snow-related variables of interest include depth, density, hardness, collapse pressure, thermal resistance, snow-onset date, snow-free date, length of snow-covered season, and many others depending on the particular application. Recent advances in Arctic snow monitoring and modeling have indeed been made (reviewed in Bokhorst et al. 2016), yet the development process of these snow products lacks interdisciplinary cooperation, which can lead to snow datasets inappropriate for, e.g., wildlife applications. In what follows, we offer a future prospectus for improving wildlife-specific snowscape products for the ABR that focuses on enhancement and integration of existing observations and modeling approaches.

pte

Ground-based measurements of snowscape properties. Given the sparseness of existing in situ snow and snow-related observations (see Fig. 1), improvement of the existing ground-based observing network should focus primarily on improving the spatial coverage and representativeness of the measurements. When selecting where to locate new stations, underrepresented landscape types and areas identified by snow models as particularly difficult to characterize should be prioritized. Areas that are most underrepresented include locations that are farthest from existing road networks and higher elevations. Unfortunately, the current trend in the total number of snow monitoring stations is flat and agencies operating them have tightening budget constraints. However, there may be opportunities to leverage existing infrastructure to meet the need for in-situ data through greater collaboration across management agencies. For example, Alaska Transportable Array infrastructure (usarray.org/alaska#after2018), located in underrepresented areas, could be identified, augmented with sensors, and operation supported to collect snowscape relevant data. Further, it is critical that augmented networks are equipped with sensors that measure the most critical variables required to make characterize snowscape properties: total precipitation, snow depth, snow water equivalence, and snow surface temperature. Finally, we suggest a complimentary, ground-based observational approach to collecting information on snowscape properties - deployment of animal-borne sensors. The state of the art in movement ecology includes several miniaturized data logging sensors, including video, audio, biological, and atmospheric data (Kays et al. 2015). When combined with GPS units, these sensors allow scientists to spatially and temporally reference a suite of co-registered data. For example, cameras have been used to record short videos on pre-programmed schedules, yielding

ce

475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 29

12

Page 13 of 29

us cri

pt

qualitative data on ground conditions (Thompson et al. 2012), with potential for remote documentation of a suite of vegetation and snowpack characteristics. In an analogous application, oceanographic sensors have been deployed on narwhals (Monodon monoceros) to make deep-water observations in Arctic waters that were otherwise unobtainable (Laidre and Heide-Jørgensen 2007). Animal-borne cameras could not only serve to augment traditional ground measurement networks, but also contribute to validation of remotely sensed snow products. Further, biotelemetry sensors that provide data on activity (e.g. accelerometers) and physiology (e.g. heart-rate or body temperature data loggers), can inform empirical relationships between snowscape properties and wildlife movement, activity, and physiological metrics. These empirical relationships can then, in turn, enable those fine-scale metrics to be used as indirect indicators of localized snow conditions throughout remote ABRs.

pte

dM

an

Remotely sensed retrievals of snowscape properties. Developing more effective snow retrievals for wildlife studies will likely involve the use of different sensor combinations, including passive optical-IR, lidar, gamma radiation, and active and passive microwave remote sensing. Promising approaches include the fusion of satellite optical-IR and multi-frequency active and passive microwave sensor observations for delineating landscape freeze-thaw heterogeneity and wet snow conditions (Podest et al. 2014; Kim et al. 2015). In addition, the use of multi-frequency satellite radar data for landscape level SWE retrievals (Rott et al. 2010) shows promise and, when combined with a measure of snow density, could be used to calculate the more wildlife-relevant snow depth. These products will offer the greatest benefit when combined with emerging high resolution land surface and digital elevation models that will provide better scaling and modeling opportunities (e.g., Noh and Howat 2015). New developments in airborne remote sensing offer the potential for significant advances in the remote sensing of snowscapes using coordinated observations from different sensors to develop and test new algorithms for more effective snow retrievals (Deems et al. 2013; Witz, 2016). For example, airborne observations of snow depth can be obtained through differencing snow-on and snow-off lidar observations (Bolton et al. 2013; Kirchner et al. 2014; Painter et al. 2016, Zheng et al. 2016), or less expensive structure from motion DSMs (Hopkinson et al. 2004; Trujillo et al. 2007; Mott et al. 2011; Deems et al. 2013, Nolan et al. 2015). These methods are especially viable for small to medium scale (10-100 km2), un-forested areas where a few seasonal data collections (e.g. peak snow depth and bare earth) are sufficient (Nolan et al. 2015, Harder et al. 2016) Larger areas with tree cover, rugged topography, and the need for multiple observations quickly add to mission complexity and cost. Airborne activities such as these offer a path forward for developing next generation satellite-based methods to monitor snowscapes at wildlife-relevant spatial and temporal scales using data such as that expected from the Advanced Topographic Laser Altimeter System (ATLAS) to be launched aboard the NASA ICESat-2 platform (see Treichler and Kaab, 2017). Because snowscape properties in ABRs are inherently very different than those in other snow landscapes (Sturm et al. 1995), an ABR-specific airborne snow campaign, conducted concurrent with ground observations - similar to NASA’s SnowEx campaign focused on mapping SWE in Colorado’s forested ecosystems (https://snow.nasa.gov/snowex) - will be essential to developing effective snow retrievals for the region.

ce

521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Model simulations of snowscape properties. To satisfy the needs of wildlife studies, existing snow-evolution models that were initially designed for climate, hydrologic, and/or avalanche

13


567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611

Data-model fusion: Combining snow products for wildlife-snow interaction studies. While it is true that each meteorological input, each ground observation, each remote sensing instrument, and each snow modeling tool includes some valuable information on snow-property distribution and evolution, each of these contributions also comes with their own collection of weaknesses. We contend that a combined approach, using atmospheric forcing from meteorological stations (re)analyses, and/or climate scenario datasets; ground-based snow measurements; remotesensing datasets; and modeling tools is required to answer the breadth of snow science and management questions being asked by the wildlife community. In such a synthesis, each meteorological dataset, ground measurement, remote sensing observation, and model result, contributes toward unveiling the “snow puzzle”. We call this “data-model fusion”, as opposed to “data assimilation”, because it embodies a synergetic extracting and merging of information obtained from different sources, in an effort to produce an outcome that is better than the outcome obtained by using the different sources independently. By merging data from meteorological, ground in situ, remotely sensed, and/or modeled data sources, the data-model fusion approach - if carefully applied - dynamically offsets weakness in any individual component dataset at any given point in space and time, while including the strength of other component datasets. Under conditions where, or when, a given data source is known to have certain limitations or has missing data, another data source can fill in. For example, given that snow-evolution models suffer often from precipitation inputs that are inadequate to reproduce observed snow distributions (see Section III), integration of snow-on-the-ground observations using data-model fusion can correct those precipitation deficiencies (e.g. Liston and Sturm 2002; Liston et al. 2008, 2018; Pedersen et al. 2017). As another example, snow-depth datasets are often available instead of the more hydrologically important SWE data (Sturm et al. 2010); this information can be used to add information to data-model fusion efforts. Collectively, the datamodel fusion system should ultimately return a well-informed and valued product. Data-model fusion is a multi-step process, where at each step there are interactions and feedbacks between 1) the observations, 2) the model configuration and representations, and 3) the modeling team (Williams et al. 2009; Keenan et al. 2011). Inherent in this process is the requirement that the model developers have intimate knowledge of the observational datasets, how they were collected, what features and processes they represent, what they contribute, and their limitations. The model developers must also have intimate knowledge of the modeling systems and how those systems relate to, and merge with, the various data inputs. This knowledge is particularly important to reduce the ultimate amount of error and uncertainty during product development. The data-model fusion system that we are advocating includes three basic and practical requirements, which emerge from limitations in current (and likely future) snow-related datasets. All of these requirements can be met with modifications to existing snow-evolution modeling tools, and with inputs from ground and remote-sensing observations. The system should:

612

1. Add physical properties to basic snow information and datasets (e.g. convert SWE data

ce

pte

dM

an

us cri

pt

applications should be modified and enhanced to include new wildlife-relevant snow variables such as snow depth, snow hardness, and ice-layer thickness, and vertical distribution within the snowpack. These models must also have the ability to be run at a wide range of spatial resolutions, depending on the wildlife application of interest; for example, as shown in our case studies, simulations of polar bear den habitat likely require much finer resolution than studies of caribou migration timing and routes.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 29

14

Page 15 of 29

us cri

pt

from a remote sensing instrument to snow depth, by adding model-simulated snow density data). 2. Using our physical understanding and model representations of snow-evolution processes, fill in missing data between snow measurements sparsely distributed in space and/or time. 3. Account for a wide range of snow-related observational datasets provided by both ground observations (e.g. SWE, snow depth, snow surface temperature), and remote sensing (SWE, snow presence or absence, fractional snow-covered area, grain size, albedo, snow surface temperature, snow wetness, and icing). Figure 5 provides an example of the data-model fusion approach we used to simulate snow depth in the Arctic National Wildlife Refuge (ANWR) for April 1, 2015. We merged snow-on-the-ground observations (Fig. 5a) with the MicroMet (Liston and Elder 2006b), 625 SnowModel (Liston and Elder 626 2006a), and SnowAssim Figure 5. (a) Snow depth (cm) 627 (Liston and Hiemstra 2008) observations (solid-color dots surrounded by white borders) 628 snow-evolution modeling collected during March-April 629 tools. The models were run 2015 in the Arctic National 630 over the 400-km by 400-km Wildlife Refuge (ANWR) and 631 domain using a 500-m grid spatially interpolated over the 632 increment and 3-hour time ANWR domain (color shades). 633 step (Fig. 5b). MicroMet (b) Snow depths (cm; color 634 ingested atmospheric shades) simulated by 635 reanalysis data, SnowAssim SnowModel on 1 April 2015, 636 assimilated end-of-winter while assimilating the snow 637 (pre-melt) SWE observations, depth observations using 638 and SnowModel performed SnowAssim. Observed snow 639 the spatial and temporal depth sites and values are 640 interpolations and evolutions included as solid-color dots 641 and added the highersurrounded by white borders. 642 resolution physics to the The ANWR boundary is also 643 distributions, including included (black line). 644 conversion from SWE to 645 snow depth (Fig. 5b).

Figure 5a displays the spatially interpolated observations (no physically based adjustments have been applied). Figure 5b shows the value added when the physics in the models are used to distribute and convert the SWE to snow depth. The data-model fusion snow-depth distribution (Fig. 5b) includes features such as 1) thinner snow along the lowlands in the south of the domain and along the Arctic coast (the purple areas), 2) less snow in the bottoms of the drainage basins, 3) deeper snow in the higher elevations, 4) precipitation-shadow effects south of the Brooks Range, and 5) vegetation-related snow distributions (e.g. forests vs tundra). All of these important snow-distribution features are largely absent in the observations (Fig. 5a). The data-model fusion example shown in Figure 5b produced spatially- and temporallycontinuous snow-property distributions that match our physical understanding of snow processes (e.g. snowfall / precipitation distributions, conservation of mass and energy, layer development within the snowpack, snow-canopy interception progression, blowing snow redistribution, snow-

ce

646 647 648 649 650 651 652 653 654 655 656 657 658

pte

dM

an

613 614 615 616 617 618 619 620 621 622 623 624

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


15


pte

dM

an

us cri

pt

vegetation interactions, melt rates and timing, snow-covered-area evolution, etc.) and the available field observations. Only one time (day; 1 April 2015) is shown, but the model simulated the snow-depth distribution every 3-hours from 1 September 2014 through 31 August 2015. In addition, while this simulation example only assimilated snow-on-the-ground observations, remote sensing observations could have also been included in the data-model fusion. This data-model fusion approach promises to be broadly applicable to a wide range of ecosystem studies in the ABR, but it is a particularly attractive option for studying wildlife-snow interactions because we expect it to deliver on all of our most pressing needs via one, consistent and effective approach. In fact, we believe there is no other option. There is no evidence to suggest we have the financial resources or ability to modify existing snow monitoring technologies (i.e., ground-based or remote-sensing systems) such that comprehensive and complete mosaics of wildlife-relevant snow variables are available for the purposes of wildlife science and management research. This forces us to turn to models. But it is also clear that these models can benefit from the observations we are making. The logical next step, and the solution to our data-deficiency problem, is to merge the data with the models and create something more than exists without this coupling. The data-model fusion approach can produce spatially detailed and temporally continuous datasets of multiple snowscape properties and variables relevant to wildlife over large areas. It can do this by merging 1) the detailed temporal coverage of snow and meteorological data from ground observations, 2) the vast spatial coverage offered by remotely sensed observations, and 3) the large suite of simulated snowscape variables from wildliferelevant snow modeling tools. Other ABR research efforts will also benefit from this data-model fusion approach. The spatial snow products that are critical to understanding wildlife behavior and fitness are also essential to understanding the influence of snow dynamics on people in the ABR. Humans are affected by the same suite of snowscape properties as wildlife. For example, dynamics in snowscape properties influence travel by residents of rural subsistence communities, and thus impact the ease and safety of access to provisioning ecosystem services such as fish, wild game, drinking water, hydro power, and firewood (Callaghan et al. 2011; Brinkman et al. 2016). As other examples, changes in snowscape conditions in wildlife habitat may alter availability of wild game to subsistence communities (Tyler 2010; Langlois et al. 2017), or limit skiing opportunities and snowmobile operation, thus affecting winter tourism (Abegg et al. 2007). At the same time, reported experiences and qualitative observations made by people working and living in ABRs can be formally included in our understanding of human-snow interactions. Currently, however, there is a dearth of information on the topic because the spatial and temporal resolution of data on both humans and snow are insufficient to estimate causal relationships. The data-model fusion approach we advocate herein is a viable mechanism to correct this data void.

ce

659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704

VI.

CONCLUSIONS

Although a large suite of ABR-wide spatial snow products is available, these products often have insufficient spatial or temporal resolutions, coverage, or lack specific snowpack properties that are most relevant to wildlife behavior. Given that snow depth, collapse pressure, layer hardness, and other wildlife-relevant factors significantly affect wildlife in snowscapes, the development, improvement, and availability of spatial products for each of these variables should be a clear and well defined focus of future wildlife-snow studies. It is equally important that the

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 29

16

Page 17 of 29

pte

dM

an

us cri

pt

spatiotemporal resolution of collected wildlife data is well-matched to that of the spatial snow products that are proposed herein. In fact, rapid technological advances in animal-borne sensors are enabling measurements of animal behavior at increasingly finer spatial and temporal resolutions, and over larger areas (Kays et al. 2015). This means that wildlife biologists are increasingly able to ask and test questions relevant at scales and resolutions that surpass those of the existing suite of widely available environmental covariates - such as snow properties - that influence the movement and behavior they seek to understand. As a specific and urgent example of this need, the NASA Arctic Boreal Vulnerability Experiment (ABoVE) is an intensive multi-year field campaign recently inaugurated across Alaska and northwestern Canada that involves coordinated observations from airborne and satellite remote sensing in context with detailed field studies and modeling (Goetz et al. 2011). A major ABoVE science objective is to quantify how changes in the spatial and temporal distribution of snow impacts ecosystem structure and function, including wildlife habitats across ABR environmental gradients. The ABoVE design provides a framework for coordinating ground and remote sensing observations with modeling approaches for producing snowscape products at multiple spatial resolutions and temporal scales. With these data sources, ABoVE could lead the way in improving snow science in the ABR by focusing its efforts and developing data-model fusion approaches to produce fit-for-purpose snow products, with particular emphasis on the, to date, underserved needs of Arctic and boreal research scientists. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of ABoVE and NASA’s SnowEx campaign, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties that may provide a testbed for developing next generation satellite missions able to monitor ABR snowscape dynamics relevant to wildlife. To accomplish this requires active development and contributions from all three constituents of the data-model fusion system: in situ ground, remotely sensed observations, and models. By working together these groups will define and make new types of measurements to develop snow-evolution modeling systems that ingest observing system variables and create wildlife-relevant outputs. Immediate investment into improving the availability of specialized, fit-for-purpose spatial snow products for the ABR is required to expand our knowledge and improve our ability to answer key snow-related wildlife science and management questions.

ACKNOWLEDGMENTS. We thank Bruno Croft, Jan Adamczewski, Allicia Kelly, Buck Mangipane (NPS Dall sheep dataset) and Adam Wells for sharing data on animal movement. This study benefited from funding from several NASA ABoVE grants (NNX15AT72A, NNX15AT74A, NNX15AT89A, NNX15AT91A, NNX15AU13A, NNX15AU20A, NNX15AU21A, NNX15AV92A, NNX15AW71A), one other NASA grant (80NSSC18K0571), as well as several NSF grants (1603777, 1556248, 1564380, 1602898).

ce

705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


17


REFERENCES.

755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776

Aikens EO, Kauffman MJ, Merkle JA, et al (2017) The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol Lett 20:741–750. doi: 10.1111/ele.12772

pt

750 751 752 753 754

us cri

Abegg, B., Agrawala, S., Crick, F. and de Montfalcon, A., 2007. Climate change impacts and adaptation in winter tourism. Climate change in the European Alps: Adapting winter tourism and natural hazards management, pp.25-58.

Alaska Department of Fish and Game. 2014. Trends in Alaska sheep populations, hunting, and harvests. Division of Wildlife Conservation, Wildlife Management Report ADFG/DWC/WMR2014-3, Juneau. Ambio. 2016 Sep; 45(5): 516–537. Published online 2016 Mar 17. doi: 10.1007/s13280-0160770-0

an

Amstrup, S.C., 2003. Polar bear, Ursus maritimus. In: Feldhamer, G.A., Thompson, B.C., Chapman, J.A. (Eds.), Wild Mammals of North America: Biology, Management, and Conservation. John Hopkins University Press, Baltimore, USA, pp. 587–610.

dM

Amstrup, S.C., Gardner, C., 1994. Polar bear maternity denning in the Beaufort Sea. J. Wildl. Manag. 58, 1–10. Bartelt, P and Lehning, M. 2002. A physical SNOWPACK model for the Swiss avalanche warning: Part I. Numerical model. – Cold Regions Science and Technology 35: 123-145. Berteaux, D., et al. 2017. Effects of changing permafrost and snow conditions on tundra wildlife: critical places and times. Arctic Science, 3(2), 65-90. Beumer LT, Varpea Ø, Hansen BB (2017) Cratering behaviour and faecal C: N ratio in relation to seasonal snowpack characteristics in a high-arctic ungulate. Polar Res. doi: 10.1080/17518369.2017.1286121

783 784 785 786 787 788 789 790 791 792

Boelman NT, Krause JS, Sweet SK, et al (2017) Extreme spring conditions in the Arctic delay spring phenology of long-distance migratory songbirds. Oecologia. doi: 10.1007/s00442-0173907-3

pte

777 778 779 780 781 782

ce

Bilodeau F, Gauthier G, Berteaux D. 2013. Effect of snow cover on the vulnerability of lemmings to mammalian predators in the Canadian Arctic. J. Mammal 94(4): 813-819.

Bokhorst, S., S.H. Pedersen, L. Bruckere, O. Anisimov, J.W. Bjerke, R.D. Brown, D. Ehrich, R.L.H. Essery, A. Heilig, S. Ingvander, C. Johansson, M. Johansson, I.S. Jonsdottir, N. Inga, K. Luojus, G. Macelloni, H. Mariash, D. McLennan, G.N. Rosqvist, A. Sato, H. Savela, M. Schneebeli, A. Sokolov, S.A. Sokratov, S. Terzago, D. Vikhamar-Schuler, S. Williamson, Y. Qiu, T.V. Callaghan, 2016. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio, 45, 516-537.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 29

18

Page 19 of 29

pt

Bolton, D.K., Coops, N.C., & Wulder, M.A. (2013). Investigating the agreement between global canopy height maps and airborne Lidar derived height estimates over Canada. Canadian Journal of Remote Sensing, 39, S139-S151 Boulanger, John, Bruno Croft, Jan Adamczewski, Dean Cluff, Mitch Campbell, David Lee, Nic Larter (2017) An estimate of breeding females and analyses of demographics for the Bathurst herd of barren-ground caribou: 2015 calving ground photographic survey. Environment and Natural Resources, Government of Northwest Territories, Manuscript Report No. 267.

us cri

793 794 795 796 797 798 799 800 801 802 803 804 805

Brinkman, T.J., Hansen, W.D., Chapin, F.S., Kofinas, G., BurnSilver, S. and Rupp, T.S., 2016. Arctic communities perceive climate impacts on access as a critical challenge to availability of subsistence resources. Climatic Change, 139(3-4), pp.413-427. Brown RD, Mote PW (2009) The response of Northern Hemisphere snow cover to a changing climate. J Clim 22:2124–2145. doi: 10.1175/2008JCLI2665.1

816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835

Chen W, White L, Adamczewski JZ, et al (2014) Assessing the Impacts of Summer Range on Bathurst Caribou’s Productivity and Abundance since 1985. Nat Resour 05:130–145. doi: 10.4236/nr.2014.54014

an

806 807 808 809 810 811 812 813 814 815

dM

Callaghan, T. V., M. Johansson, R. D. Brown, P. Y. Groisman, N. Labba, V. Radionov, R. G. Barry, O. N. Bulygina, R. L. H. Essery, D. M. Frolov, V. N. Golubev, T. C. Grenfell, M. N. Petrushina, V. N. Razuvaev, D. A. Robinson, P. Romanov, D. Shindell, A. B. Shmakin, S. A. Sokratov, S. Warren, and D. Yang. 2011. The changing face of Arctic snow cover: A synthesis of observed and projected changes. Ambio 40:17-31. CDEC, 2017, cdec.water.ca.gov/cdecstation2/

pte

Collins, W. B. and T. S. Smith (1991) Effects of Wind-Hardened Snow on Foraging by Reindeer (Rangifer tarandus), Arctic, Vol. 44, No. 3 (Sep., 1991), pp. 217-222. Comiso, J. C., Parkinson, C. L., Gersten, R., Stock, L., 2008. Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett. 35, L01703, http://dx.doi.org/10.1029/2007GL031972.

ce

Craighead, F.C., Jr. and Craighead, J.J. (1972) Grizzly bear prehibernation and denning activities as determined by radiotracking and denning activities as determined by radiotracking. Wildlife Monographs, 32. Dailey, T. V. and Hobbs, N. T. (1989). Travel in alpine terrain: energy expenditures for locomotion by mountain goats and bighorn sheep. Canadian Journal of Zoology, 67, 2368–2375. https://doi.org/10.1139/z89-335

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Dee, D.P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., & Bauer, P. (2011). The ERA-Interim reanalysis: Configuration

19


and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553-597

844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880

Derksen C, Brown R (2012) Spring snow cover extent reductions in the 2008-2012 period exceeding climate model projections. Geophys Res Lett 39:1–6. doi: 10.1029/2012GL053387

pt

836 837 838 839 840 841 842 843

us cri

Deems, J.S., T.H. Painter, and D.C. Finnegan, 2013. Lidar measurement of snow depth: a review. The Journal of Glaciology, 59, 215, 467-478.

Deems, J.S., Painter, T.H., Finnegan, D.C., 2013a. Lidar measurement of snow depth: a review. J. Glaciol. 59 (215), 467–479. http://dx.doi.org/10.3189/2013JoG12J154.

Dietz, A.J., Kuenzer, C., Gessner, U. and Dech, S., 2012. Remote sensing of snow–a review of available methods. International Journal of Remote Sensing, 33(13), pp.4094-4134.

an

Duchesne D, Gauthier G, Berteaux D. 2011. Habitat selection, reproduction and predation of wintering lemmings in the Arctic. Oecologia 167(4): 967-98 Duquette, L.S. 1988. Snow characteristics along caribou trails and within feeding areas during spring migration. Arctic, 41: 143–144

dM

Durand, Y., Giraud, G., Brun, E., Merindol, L. and Martin, E. 1999. A computer-based system simulating snowpack structures as a tool for regional avalanche forecasting. Journal of Glaciology. Vol. 45. No. 151. 469-484. Eaton, L. A., and S. Businger, 2014: Using a snow drift model to simulate eolian drift and snowfall on the summit of Mauna Kea, Hawaii. Arctic, Antarctic, and Alpine Research, 46(4), 719-734.

pte

Fancy, S.G. and Robert G. White (1985) Energy Expenditures by Caribou while Cratering in Snow. The Journal of Wildlife Management, 49(4): 987-993 Fancy, S.G., and White, R.G. 1987. Energy expenditures for locomotion by barren-ground caribou. Can. J. Zool. 65: 122–128. doi:10.1139/z87-018

ce

Forchhammer, Mads and David Boertmann (1993) The Muskoxen Ovibos Moschatus in North and Northeast Greenland: Population Trends and the Influence of Abiotic Parameters on Population Dynamics. Ecography, Vol. 16, No. 4, pp. 299-308. Formozov, A. 1946. Snow cover as an environmental factor and its importance in the life of mammals and birds. Moscow Society of Naturalists, materials for the Study of Fauna and Flora USSR, Zoology Section, N.S. 5. 141 pp.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 29

Girotto, M., G. Cortés, S. A. Margulis, and M. Durand, 2014: Examining spatial and temporal variability in snow water equivalent using a 27 year reanalysis: Kern River watershed, Sierra Nevada. Water Resour. Res., 50, 6713–6734, doi:10.1002/2014WR015346.

20

Page 21 of 29

Goetz SJ, Epstein HE, Bhatt US, et al (2011) Eurasian Arctic Land Cover and Land Use in a Changing Climate. Arctic 9–36. doi: 10.1007/978-90-481-9118-5

886 887 888

Grabowski MM, Doyle FI, Reid DG, et al (2013) Do Arctic-nesting birds respond to earlier snowmelt? A multi-species study in north Yukon, Canada. Polar Biol 36:1097–1105. doi: 10.1007/s00300-013-1332-6

889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904

Haber, G. C. 1977 Socio-ecological dynamics of wolves and prey in a subarctic ecosystem. Ph.D. thesis, University of British Columbia, Vancouver.

905 906 907

Hopkinson C, Sitar M, Chasmer L, Treitz P (2004) Mapping Snowpack Depth beneath Forest Canopies Using Airborne Lidar. Photogramm Eng Remote Sens 70:323–330. doi: 10.14358/PERS.70.3.323

908 909

Ims R a., Fuglei E (2005) Trophic Interaction Cycles in Tundra Ecosystems and the Impact of Climate Change. Bioscience 55:311. doi: 10.1641/0006-3568(2005)055[0311:TICITE]2.0.CO;2

910 911 912 913 914 915 916 917 918 919

Johnson CJ, Parker KL, Heard DC (2001) Foraging across a variable landscape: Behavioral decisions made by woodland caribou at multiple spatial scales. Oecologia 127:590–602. doi: 10.1007/s004420000573 Kausrud, K.L., Mysterud, A., Steen, H., Vik, J.O., Ostbye, E., Cazelles, B., Framstad, E., Eikeset, A.M., Mysterud, I., Solhoy, T. and Stenseth, N.C. (2008) Linking climate change to lemming cycles. Nature 456: 93-U3. Kays R1, Crofoot MC2, Jetz W3, Wikelski M4., Science. 2015 Jun 12;348(6240):aaa2478. doi: 10.1126/science.aaa2478. ECOLOGY. Terrestrial animal tracking as an eye on life and planet.

pt

881 882 883 884 885

us cri

Goodison, B. E., H. L. Ferguson, and G. A. McKay, 1981: Measurement and data analysis. Handbook of Snow, D. M. Gray and M. D. Male (Eds.), Pergamon Press, 191 274.

Hansen, B. B. et al. 2011. Climate, icing, and wild arctic reindeer: past relationships and future prospects. — Ecology 92: 1917–1923.

an

Hansen, B. B. et al. 2013. Climate events synchronize the dynamics of a resident vertebrate community in the high Arctic. — Science 339: 313–315.

dM

Harder, P., Schirmer, M., Pomeroy, J., and Helgason, W.: Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle, The Cryosphere, 10, 25592571, https://doi.org/10.5194/tc-10-2559-2016, 2016

ce

pte

Hoefs, M., & McTaggart-Cowan, Ian. (1980). Ecological investigation of a population of Dall sheep (Ovis dalli dalli Nelson) (Syesis ; v. 12, suppl. 1). Victoria, B.C.]: British Columbia Provincial Museum.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


21


pt

Keenan TF, Carbone MS, Reichstein M, Richardson AD (2011) The model-data fusion pitfall: Assuming certainty in an uncertain world. Oecologia 167:587–597. doi: 10.1007/s00442-0112106-x

us cri

Kelsall,, J.P. and Prescott, W. (1971) Moose and deer behaviour in snow in Fundy National Park, New Brunswick, Rep. Ser. Can. Wildl. Serv.,No.15 pp.25. Kelsall, J.P., and E.S. Telfer (1971) Studies of the physical adaptation of big game for snow. Proc. Snow Ice Relation Wildl. Recreation Symp., Iowa State Univ, Ames, Iowa, 134-146. Kim, Y., Kimball, J. and McDonald, K. (2012) MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status, Version 4 [1979 to 2010]. National Snow and Ice Data Center, Boulder, CO, USA, NASA National Snow and Ice Data Center Distributed Active Archive Center. DOI: 10.5067/MEASURES/CRYOSPHERE/nsidc-0477.004.

an

Kim, Y., J.S. Kimball, D.A. Robinson, and C. Derksen (2015) New satellite climate data records indicate strong coupling between recent frozen season changes and snow cover over high northern latitudes. Environmental Research Letters 10, 084004

dM

Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.: LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci., 18, 4261-4275, DOI: 10.5194/hess-18-4261-2014, 2014. Koizumi, C. L., J. Carey, M. Branigan, and K. Callaghan. 2011. Status of Dall’s sheep (Ovis dalli dalli) in the Northern Richardson Mountains. Yukon Fish and Wildlife Branch Report TRC11-01. Whitehorse, Yukon, Canada Kwok, R., Rothrock, D.A., 2009. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophys. Res. Lett. 36, L15501, http://dx.doi.org/10.1029/2009GL039035.

pte

Laidre, K.L., and M.P. Heide-Jørgensen. 2007. Using narwhals as ocean-observing platforms in the high Arctic. Oceanography 20(4):30–35, https://doi.org/10.5670/oceanog.2007.02. Langlois, A., Johnson, C.A., Montpetit, B., Royer, A., Blukacz-Richards, E.A., Neave, E., Dolant, C., Roy, A., Arhonditsis, G., Kim, D.K. and Kaluskar, S., 2017. Detection of rain-onsnow (ROS) events and ice layer formation using passive microwave radiometry: A context for Peary caribou habitat in the Canadian Arctic. Remote Sensing of Environment, 189, pp.84-95.

ce

920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963

Laperriere, J. and Peter C. Lent (1977) Caribou Feeding Sites in Relation to Snow Characteristics in Northeastern Alaska. Arctic, Vol. 30, No. 2, pp. 101-108. Le Corre, M., Dussault, C., and Côté, S.D. 2017. Weather conditions and variation in timing of spring and fall migrations of migratory caribou. J. Mammal. 98(1): 260–271.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 29

22

Page 23 of 29

pt

us cri

Lendrum, P. E., J. M. Northrup, C. R. Anderson, G. E. Liston, C. L. Aldridge, K. R. Crooks, and G. Wittemyer, 2017: Predation risk across a dynamic landscape: effects of anthropogenic land use, natural landscape features, and prey distribution. Landscape Ecol., DOI 10.1007/s10980017-0590-z. Liebezeit JR, Gurney KEB, Budde M, et al (2014) Phenological advancement in arctic bird species: Relative importance of snow melt and ecological factors. Polar Biol 37:1309–1320. doi: 10.1007/s00300-014-1522-x Lindsay, C., Zhu, J., Miller, A.E., Kirchner, P., & Wilson, T.L. (2015). Deriving snow cover metrics for Alaska from MODIS. Remote Sensing, 7, 12961-12985

an

Liston, G.E., & Elder, K. (2006a). A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology, 7, 1259-1276

dM

Liston, G. E., and K. Elder, 2006b: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). J. Hydrometeorology, 7, 217-234. Liston, G. E., R. B. Haehnel, M. Sturm, C. A. Hiemstra, S. Berezovskaya, and R. D. Tabler, 2007: Simulating complex snow distributions in windy environments using SnowTran-3D. Journal of Glaciology, 53, 241-256. Liston, G. E., and C. A. Hiemstra, 2008: A simple data assimilation system for complex snow distributions (SnowAssim). J. Hydrometeorology, 9, 989-1004. Liston, G.E. and C.A. Hiemstra, 2011: The Changing Cryosphere: Pan-Arctic Snow Trends (1979–2009). J. Climate, 24, 5691–5712, https://doi.org/10.1175/JCLI-D-11-00081.1

pte

972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008

Lehning, M, Völksch, I, Gustafsson, D, Nguyen, T, Stähli, M, Zappa, M, 2006. ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology. Hydrol. Processes. 20, 2111-2128.

Liston, G. E., C. A. Hiemstra, K. Elder, and D. W. Cline, 2008: Meso-cell study area (MSA) snow distributions for the Cold Land Processes Experiment (CLPX). J. Hydrometeorology, 9, 957-976. Liston, G.E., Hiemstra, C.A., & Sturm, M. (2016). SnowModel Pan-Arctic Data 1979-2009. Arctic Data Center. doi:10.5065/D6DV1H19

ce

964 965 966 967 968 969 970 971

Liston, G. E., J. P. McFadden, M. Sturm, and R. A. Pielke, Sr., 2002: Modeled changes in arctic tundra snow, energy, and moisture fluxes due to increased shrubs. Global Change Biology, 8, 1732.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Liston, G. E., C. J. Perham, R. T. Shideler, and A. N. Cheuvront (2016) Modeling snowdrift habitat for polar bear dens. Ecological Modelling, 320, 114-134.

23


pt

Liston, G. E., C. Polashenski, A. Rösel, P. Itkin, J. King, I. Merkouriadi, and J. Haapala, 2017: A distributed snow evolution model for sea ice applications (SnowModel). J. Geophys. Res. Oceans. In review.

us cri

Liston, G. E., and M. Sturm, 2002: Winter precipitation patterns in arctic Alaska determined from a blowing snow model and snow depth observations. J. Hydrometeor., 3, 646-659. Macander, M.J., Swingley, C.S., Joly, K., & Raynolds, M.K. (2015). Landsat-based snow persistence map for northwest Alaska. Remote Sensing of Environment, 163, 23-31

Macander, M., Swingley, C., Parr, C., Sturm, M., Selkowitz, D., & Larsen, C. (2016). Snow depth and snow persistence patterns in the Arctic from analysis of the entire Landsat archive. In, AGU Fall Meeting Abstracts

an

MacGillivray, A. O., Hannay, D. E., Racca, R. G., Perham, C. J., MacLean, S. A., Williams, M.T., 2003. Assessment of Industrial Sounds and Vibrations Received in Artificial Polar Bear Dens, Flaxman Island, Alaska. Final Report to Exxon Mobil ProductionCo. by JASCO Research Ltd., Victoria, British Columbia and LGL Alaska Research Associates, Inc., Anchorage, Alaska, USA.

dM

Mahoney, PJ, G Liston, S LaPoint, E Gurarie, B Mangipane, AG Wells, TJ Brinkman, JUH Eitel, N Boelman, LR Prugh. Submitted. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecological Applications. Margulis, S. A., M. Girotto, G. Cortés, and M. Durand, 2015: A particle batch smoother approach to snow water equivalent estimation. J. Hydrometeorol., 16(4), 1752–1772, doi:10.1175/JHM-D-14-0177.1. Maslanik, J., Stroeve, J., Fowler, C., Emery, W., 2011. Distribution and trends in Arctic sea ice age through spring 2011. Geophys. Res. Lett. 38, L13502, http://dx.doi.org/10.1029/2011GL047735.

pte

1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044

Mech, L. D., L. D. Frenzel, Jr., and P. D. Karns. 1971. The effect of snow conditions on the vulnerability of white-tailed deer to predation. Pages 51-59 in L. D. Mech and L. D. Frenzel, Jr., eds. Ecological studies of the timber wolf in northeastern Minnesota. U.S.D.A. Forest Service Research Paper NC-52. North Central Forest Experiment Station, St. Paul, Minnesota. Meltofte H, Høye TT, Schmidt NM, Forchhammer MC (2007) Differences in food abundance cause inter-annual variation in the breeding phenology of High Arctic waders. Polar Biol 30:601–606. doi: 10.1007/s00300-006-0219-1

1048 1049

Mills LS, Zimova M, Oyler J, et al (2013) Camouflage mismatch in seasonal coat color due to decreased snow duration. Proc Natl Acad Sci 110:7360–7365. doi: 10.1073/pnas.1222724110

ce

1045 1046 1047

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 29

24

Page 25 of 29

pt

Mott, R., Schirmer, M., Lehning, M., 2011. Scaling properties of wind and snow depth distribution in an Alpine catchment. J. Geophys. Res. - Atmos. 116. http://dx.doi.org/10.1029/2010JD014886.

us cri

Nichols, L. and Bunnell, F. (1999). Natural history of thinhorn sheep. In R. Valdez & P. R. Krausman (Eds.), Mountain sheep of North America (pp. 23–77). Tucson, Arizona: The University of Arizona Press.

Nicholson, K.L., Arthur, S.M., Horne, J.S., and Garton, E.O. 2016. Modeling caribou movements: seasonal ranges and migration routes of the Central Arctic Herd. PLoS One, 11(4): e0150333. doi:10.1371/journal.pone.0150333.

an

Nishi, J., B. Croft, J. Williams, J. Boulanger, and D. Johnson. (2007) An estimate of breeding females in the Bathurst herd of barren ground caribou, June 2006. File report no 137, Department of Environment and Natural Resources, Government of Northwest Territories, Yellowknife, NWT.

dM

Noh, Myoung-Jong & Ian M. Howat (2015) Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions, GIScience & Remote Sensing Vol. 52 , Iss. 2, DOI:10.1080/15481603.2015.1008621 Nolan, M., Larsen, C., & Sturm, M. (2015). Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry. Cryosphere Discussions, 9 Nolin, A.W., 2010. Recent advances in remote sensing of seasonal snow. The Journal of Glaciology, 56, 200, 1141-1150. Northrup, J. M., Anderson, C. R. and Wittemyer, G. (2016), Environmental dynamics and anthropogenic development alter philopatry and space-use in a North American cervid. Diversity Distrib., 22: 547–557. doi:10.1111/ddi.12417.

pte

1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086

Olsen, M. S., T. V. Callaghan, J. D. Reist, L. O. Reiersen, D. Dahl-Jensen, M. A. Granskog, B. Goodison, G. K. Hovelsrud, M. Johansson, R. Kallenborn, J. Key, A. Klepikov, W. Meier, J. E. Overland, T. D. Prowse, M. Sharp, W. F. Vincent, and J. Walsh. 2011. The changing Arctic cryosphere and likely consequences: An overview. Ambio 40:111-118. Ouellet, F. Langlois, A., Blukacz-Richards, E.A., Johnson, C.A., Royer, A., Neave, E. and Larter, N.C. 2016. Spatialization of the SNOWPACK snow model for the Canadian Arctic to assess Peary caribou winter grazing conditions. Physical Geography 38(2): 143-158.

1090 1091 1092

Painter TH, Berisford DF, Boardman JW, et al (2016) The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sens Environ 184:139–152. doi: 10.1016/j.rse.2016.06.018

ce

1087 1088 1089

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


25


pt

Painter, T.H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R.E., & Dozier, J. (2009). Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113, 868-879

us cri

Pan, M., and 13 others, 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res., 108, 8850, doi:10.1029/2003JD003994, D22.

Parker, K. L., P. S. Barboza, and M. P. Gillingham. 2009. Nutrition integrates environmental responses of ungulates. Functional Ecology 23: 57–69. Parker, K. L., Robbins, C. T., & Hanley, T. A. (1984). Energy expenditures for locomotion by mule deer and elk. Journal of Wildlife Management, 48(2), 474–488. https://doi.org/10.2307/3801180

an

Pedersen, S. H., G. E. Liston, M. P. Tamstorf, J. Abermann, M. Lund, and N. M. Schmidt (2017) Quantifying snow controls on vegetation greenness. In review. Penczykowski RM, Connolly BM, Barton BT (2017) Winter is changing: Trophic interactions under altered snow regimes. Food Webs 13:80–91, doi: 10.1016/j.fooweb.2017.02.006

dM

1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119

Peterson and Allen (1974) Snow conditions as a parameter in moose-wolf relationships. Nat. Can. (Que.), 101:481-492. Podest, E., K.C. McDonald, and J.S. Kimball, 2014. Multi-sensor microwave sensitivity to freeze-thaw dynamics across a complex boreal landscape. IEEE TGARS 52 (11), 6818-6828. Polyakov I V., Walsh JE, Kwok R (2012) Recent changes of Arctic multiyear sea ice coverage and the likely causes. Bull Am Meteorol Soc 93:145–151. doi: 10.1175/BAMS-D-11-00070.1

1122 1123 1124 1125 1126

Pomeroy, J.W. and E. Brun. 2001. Physical properties of snow Chapter 2, In Snow ecology, ed. H. G. Jones. Cambridge: Cambridge University Press.

1127 1128

Putkonen J, Grenfell TC, Rennert K, et al (2009) Rain on snow: Little understood killer in the North. Eos (Washington DC) 90:221–222. doi: 10.1029/2009EO260002

1129 1130 1131 1132

Rasmus, S., Kivinen, S., Bavay, M. and Heiskanen, J. 2016. Local and regional variability in snow conditions in northern Finland: a reindeer herding perspective. Ambio 45(4): 398-414. DOI: 10.1007/s13280-015-0762-5

pte

1120 1121

ce

Pruitt, W. O. 1959. Snow as a factor in the winter ecology of the barren ground caribou (Rangifer arcticus). Arctic 12:159-179.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 29

26

Page 27 of 29

Reid DG, Bilodeau F, Krebs CJ, et al (2012) Lemming winter habitat choice: A snow-fencing experiment. Oecologia 168:935–946. doi: 10.1007/s00442-011-2167-x

1151 1152 1153 1154

Rott H., S.Yueh, D.W. Cline, C. Duguay, R. Essery, C. Haas, F. Hélière, M. Kern, G. Macelloni, E. Malnes, T. Nagler, J. Pulliainen, H. Rebhan, A. Thompson, “Cold Regions Hy- drology Highresolution Observatory for Snow and Cold Land Processes”, IEEE Transactions on geoscience and remote sensing, 99, 1-10, 2010.

us cri

Rennert, K. J., G. Roe, J. Putkonen, and C. M. Bitz. 2009. Soil Thermal and Ecological Impacts of Rain on Snow Events in the Circumpolar Arctic. Journal of Climate 22:2302-2315. Rienecker, M.M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M.G., Schubert, S.D., Takacs, L., & Kim, G.-K. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24, 3624-3648

dM

an

Robinson, David A.; Estilow, Thomas W.; and NOAA CDR Program (2012): NOAA Climate Data Record (CDR) of Northern Hemisphere (NH) Snow Cover Extent (SCE), Version 1. Monthly snow area, NOAA National Centers for Environmental Information. doi:10.7289/V5N014G9, Mar. 2018.

Selkowitz, D.J., & Forster, R.R. (2015). An automated approach for mapping persistent ice and snow cover over high latitude regions. Remote Sensing, 8, 16 Simmons, N. 1982. Seasonal ranges of Dall’s sheep, Mackenzie Mountains, Northwest Territories. Arctic. Skogland, T., (1978), Characteristics of the Snow Cover and Its Relationship to Wild Mountain Reindeer (Rangifer tarandus tarandus L.) Feeding Strategies. Arctic and Alpine Research Vol. 10, No. 3 (Aug., 1978), pp. 569-579

pte

1165 1166 1167 1168 1169 1170 1171 1172 1173 1174

Reinking, A. K., K. T. Smith, K. L. Monteith, T. W. Mong, M. J. Read, and J. L. Beck (2017), Do intrinsic, environmental, or anthropogenic factors influence pronghorn summer mortality? Journal of Wildlife Management, in press.

Smith, P.A., Gilchrist, H. Grant, Forbes, Mark R., Martin, Jean-Louis AK (2010) Inter-annual variation in the breeding chronology of arcticshorebirds: effects of weather, snow melt and predators. J Avian Biol 41:292–304. doi: 10.1111/j.1600-048X.2009.04815.x

ce

1155 1156 1157 1158 1159 1160 1161 1162 1163 1164

pt

1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150

Sokolov, A. A., Sokolova, N. A., Ims, R. A., Brucker, L., & Ehrich, D. (2016) Emergent rainy winter warm spells may promote boreal predator expansion into the Arctic. Arctic, 69(2), 121. Stelfox and Taber 1968 Stien, A., Loe, L.E., Mysterud, A., Severinsen, T., Kohler, J., Langvatn, R. (2010) Icing events trigger range displacement in a high-arctic ungulate. Ecology. Mar;91(3):915-20.

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1175

27


1176 1177 1178 1179 1180 1181 1182 1183

Stirling, I., Derocher, A. E. (2012) Effects of climate warming on polar bears: a review of the evidence. Glob. Change Biol. 18, 2694–2706.

1184 1185 1186

Sturm M, Holmgren J, Liston GE (1995) A seasonal snow cover classification system for local to global applications. J Clim 8:1261–1283. doi: 10.1175/15200442(1995)0082.0.CO;2

1187 1188

Sturm, M, B. Taras, G. E. Liston, C. Derksen, T. Jonas, and J. Lea (2010) Estimating snow water equivalent using snow depth data and climate classes. J. Hydrometeorology, 11, 1380-1394.

1189 1190

Tape KD, Lord R, Marshall H-P, Ruess RW (2010) Snow-Mediated Ptarmigan Browsing and Shrub Expansion in Arctic Alaska. Ecoscience 17:186–193. doi: 10.2980/17-2-3323

1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215

Telfer ES, Kelsall JP, Telfer ES, Kelsall JP (2016) Adaptation of some large North American mammals for survival in snow, Ecology, 65:1828–1834.

us cri

pt

Stien, A. et al. (2012) Congruent responses to weather variability in high Arctic herbivores. Biol. Lett. 8, 1002–1005.

dM

an

Sturm, M., 2012: A Prototype Network for Measuring Arctic Winter Precipitation and Snow Cover (SnowNet 1 and 2). Arctic Data Center.

Tedesco, M. et al. Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data. The Cryosphere 7, 615–630 (2013).

pte

Thompson, I. D., Bakhtiari, M., Rodgers, A. R., Baker, J. A., Fryxell, J. M. & Iwachewski, E. (2012), Application of a high-resolution animal-borne remote video camera with global positioning for wildlife study: Observations on the secret lives of woodland caribou, Wildlife Society Bulletin, 36(2), 365-370. Treichler, D. and Kaab, A. (2017) Snow depth from ICESat laser altimetry - A test study in southern Norway, Remote Sensing of Environment, 191, 389-401 DOI: 10.1016/j.rse.2017.01.022

ce

Trujillo, E. J.A. Ramirez, K.J. Elder (2007) Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields, Water Resour. Res., 43 Tyler, N.J., 2010. Climate, snow, ice, crashes, and declines in populations of reindeer and caribou (Rangifer tarandus L.). Ecological Monographs, 80(2), pp.197-219. Varhola, A., Coops, N.C., Bater, C.W., Teti, P., Boon, S., & Weiler, M. (2010). The influence of ground-and lidar-derived forest structure metrics on snow accumulation and ablation in disturbed forests. Canadian Journal of Forest Research, 40, 812-821

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 29

28

Page 29 of 29

Verbyla, D., Hegel, T., Nolin, A.W., van de Kerk, M., Kurkowski, T.A., & Prugh, L.R. (2017) Remote Sensing of 2000–2016 Alpine Spring Snowline Elevation in Dall Sheep Mountain Ranges of Alaska and Western Canada. Remote Sensing, 9, 1157

1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248

Vroom, G. W., Herrero, S., Ogilvie RT (1980) The Ecology of Winter Den Sites of Grizzly Bears in Banff National Park, Alberta. 4:321–330.

us cri

Vikhamar-Schuler, D., Hanssen-Bauer, I., Schuler, T.V., Mathiesen, S.D. and Lehning, M. 2013. Use of a multi-layer snow model to assess grazing conditions for reindeer. Annals of Glaciology 54(62). DOI: 10.3189/2013AoG62A306

Williams, M., Richardson A. D., Reichstein M., Stoy P. C., and Peylin P. (2009) Improving land surface models with FLUXNET data, Biogeosciences, 6:1341–1359.

an

Winski, Dominic Erich Osterberg, David Ferris, Karl Kreutz, Cameron Wake, Seth Campbell, Robert Hawley, Samuel Roy, Sean Birkel, Douglas Introne & Michael Handley, Scientific Reports 7, Article number: 17869 (2017), doi:10.1038/s41598-017-18022-5 Witze, A. (2016) Snow sensors seek best way to track the white stuff: airborne experiments aim to fill in the blanks of global water resources as the climate changes. Nature 532, 7597, 17.

dM

Wrzesien, M. L., M. T. Durand, T. M. Pavelsky, I. M. Howat, S. A. Margulis, and L. S. Huning, 2017: Comparison of methods to estimate snow water equivalent at the mountain range scale: A case study of the California Sierra Nevada, Journal of Hydrometeorology, 18, 1101–1119, doi:10.1175/JHM-D-16-0246.1. Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev, R. Bates, T. Pangburn, and C. L. Hanson, 1998: Accuracy of NWS 8" standard nonrecording precipitation gauge: results and application of WMO intercomparison. Journal of Atmospheric and Oceanic Technology, 15, 5468.

pte

Zheng, Z., Kirchner, P. B., and Bales, R. C.: Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data, The Cryosphere, 10, 257-269, https://doi.org/10.5194/tc-10-257-2016, 2016. Zimova M, Mills LS, Lukacs PM, Mitchell MS (2014) Snowshoe hares display limited phenotypic plasticity to mismatch in seasonal camouflage. Proc R Soc B Biol Sci 281:20140029– 20140029. doi: 10.1098/rspb.2014.0029

ce

1249 1250 1251 1252

pt

1216 1217 1218 1219 1220 1221 1222

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


29