Can a snow structure model estimate snow characteristics relevant to reindeer husbandry?

Rangifer, 34, (1) 2014 32 (1), 2012 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no Rangifer, 34, (1), 2014: 37-56 37 Introduction Semi-domesticated reindeer (Rangifer tarandus tarandus) in northern Finland live in an environment where continuously changing weather and foraging conditions significantly affect populations. In particular, reindeer herds must forage for food beneath the snow for six (southern herds) to eight (northern herds) months a year (Solantie et al., 1996), with especially juvenile survival highly dependent on adequate Can a snow structure model estimate snow characteristics relevant to reindeer husbandry? Sirpa Rasmus1,2, Jouko Kumpula3 & Jukka Siitari3 1 Department of Biological and Environmental Sciences, P.O. Box 35 (Survontie 9), 40014 University of Jyväskylä, Finland (Corresponding author: [email protected]). 2 Finnish Game and Fisheries Research Institute, Jyväskylä Unit, Survontie 9, 40500 Jyväskylä, Finland. 3 Finnish Game and Fisheries Research Institute, Reindeer Research Unit, Toivoniementie 246, 99910 Kaamanen, Finland. Abstract: Snow affects foraging conditions of reindeer e.g. by increasing the energy expenditures for moving and digging work or, in contrast, by making access of arboreal lichen easier. Still the studies concentrating on the role of the snow pack structure on reindeer population dynamics and reindeer management are few. We aim to find out which of the snow characteristics are relevant for reindeer in the northern boreal zone according to the experiences of reindeer herders and is this relevance seen also in reproduction rate of reindeer in this area. We also aim to validate the ability of the snow model SNOWPACK to reliably estimate the relevant snow structure characteristics. We combined meteorological observations, snow structure simulations by the model SNOWPACK and annual reports by reindeer herders during winters 1972-2010 in the Muonio reindeer herding district, northern Finland. Deep snow cover and late snow melt were the most common unfavorable conditions reported. Problematic conditions related to snow structure were icy snow and ground ice or unfrozen ground below the snow, leading to mold growth on ground vegetation. Calf production percentage was negatively correlated to the measured annual snow depth and length of the snow cover time and to the simulated snow density. Winters with icy snow could be distinguished in three out of four reported cases by SNOWPACK simulations and we could detect reliably winters with conditions favorable for mold growth. Both snow amount and also quality affects the reindeer herding and reindeer reproduction rate in northern Finland. Model SNOWPACK can relatively reliably estimate the relevant structural properties of snow. Use of snow structure models could give valuable information about grazing conditions, especially when estimating the possible effects of warming winters on reindeer populations and reindeer husbandry. Similar effects will be experienced also by other arctic and boreal species. Key words: calf production; reindeer; Rangifer tarandus tarandus; snow; snow structure; snow modeling. Rangifer, 34, (1) 2014 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 32 (1), 2012 38 winter forage (Holleman et al., 1979). This in turn is affected both by the amount of the main winter forage, (reindeer lichens Cladina spp.), and also by the snow conditions on pastures (Skogland, 1978; Helle & Tarvainen, 1984; Kumpula, 2001). Both reindeer and its northern American relative, caribou (Rangifer tarandus), are morphologically and behaviorally adapted to Arctic ecosystems (Telfer & Kensall, 1984). Reindeer herders acknowledge the effects of weather and snow conditions on wellbeing of their herds, and husbandry has always been relatively adaptable to what comes to intraand inter-annual variations in grazing conditions (Tyler et al., 2007; Roturier & Roue, 2009; Riseth et al., 2010; Vuojala-Magga et al., 2011). Despite this, the deep snow cover and late snow melt in spring can cause high winter mortality (Adamczewski et al., 1988; Kumpula & Colpaert, 2003; Helle & Kojola, 2008) and low calf production (Adams & Dale 1998; Post & Stenseth, 1999; Aanes et al., 2000; Kumpula, 2001) of both caribou and reindeer. In addition to amount of snow, the structural properties of snow are also important. The energy required for digging effort is greater with increasing snow density and hardness (Fancy & White, 1985; Kumpula et al., 2004). Extensive ground ice (due to thawing-freezing at the snow-ground interface) has been observed to decrease the reproduction rates of Svalbard reindeer (Rangifer tarandus platyrhynchus) population (Hansen et al., 2011) or even cause population crashes (Helle, 1980; Kohler & Aanes, 2004). In addition, the number of warm days (mean T > 0 °C) during early winter or the winter time rain events, which is assumed to lead to dense or icy snow cover have been shown to decrease the calf production and winter survival of reindeer (Lee et al., 2000; Solberg et al., 2001; Kumpula & Colpaert, 2003; Helle & Kojola, 2008). Damages to reindeer by predation are partly connected to snow conditions. Majority of previous research has been based on measurements on snow depth and meteorological observations that have daily or rougher time scales. It is difficult to identify winters with icy snow cover using this kind of observations only (Helle & Kojola, 2008; VikhamarSchuler et al., 2013). In Vikhamar-Schuler et al. (2013), a snow structure model SNOWPACK was successfully used to simulate the evolution of the snow cover, especially high-density layers, during years 1956-2010 in Kautokeino (Guovdageaidnu), Northern Norway. SNOWPACK (Bartelt & Lehning, 2002; Lehning et al., 2002a and 2002b) is a widely used model for describing the development of snow mass and energy balance during the winter. It is one of the few existing snow structure models and enables to estimate the layered structure within the snow cover and physical properties (e.g. density, hardness, grain size, grain type and bonding between the grains) of the layers. In this work we used combination of detailed meteorological information, snow structure simulations by the model SNOWPACK and the annually made reindeer herders’ reports to create a comprehensive view on snow conditions in a selected reindeer herding district in Muonio, northern Finland. Due to an intensive management system relatively reliable estimates on annual mortality and productivity of Scandinavian reindeer population are available. Also winter conditions, including difficult snow condition, are annually reported by reindeer herders. Unfavourable snow and weather conditions affect in a similar way to other northern ungulates, and more broadly, to several arctic and boreal species. The global mean temperature is predicted to increase by 1.4 – 6.4 °C by the end of the year 2100 (IPCC, 2007). This warming will most likely be most extreme during winters in north-eastern Europe, and precipitation (consisting of rain on snow during warm winters) is expected to increase. These changes will alter Rangifer, 34, (1) 2014 32 (1), 2012 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 39 the amount and structure of snow cover, as well as in the length of the snow season, in many locations (Venäläinen et al., 2001; Räisänen, et al., 2003; ACIA, 2004; Rasmus et al., 2004; Kellomäki et al., 2010). Used together with climate model output data, SNOWPACK can work as a tool in climate impact studies. Therefore, it is important to validate this modelling tool in present day conditions and to examine its development needs. We aim to answer the following questions: t8IJDIPGUIFTOPXDIBSBDUFSJTUJDTBSFSFMevant for reindeer herding in northern boreal zone according to the experiences of reindeer herders? t*TUIJTSFMFWBODFTFFOBMTPJOSFQSPEVDUJPOrate of reindeer in this area? t*TJUQPTTJCMFUPVTFUIF4/081"$,NPEFMto reliably estimate the relevant snow structure characteristics within the study area? t%PFTBTOPXNPEFMBEEJOGPSNBUJPOPOTOPXand foraging conditions by reindeer compared to the conventional meteorological observations? Materials and methods Study area The Muonio reindeer herding district (2670 km2) is located in the northern boreal zone representing typical herding districts in middle parts of Finnish Lapland (Fig. 1). Snow conditions are rather homogenous through the district. Reindeer are mainly grazed on the natural pastures in Muonio, even though supplementary winter feeding has gradually increased. According to the reindeer pasture inventory conducted during 2005–2008, 27.5% of the land area is covered by ground lichen pastures, 38.7% by mature and old coniferous forests with arboreal lichen, 20.1% by dwarf shrub and graminoid vegetation and 27.5% by mires (Kumpula et al., 2009). Only small fraction of the land area is high elevation (>300 m.a.s.l), tundra vegetation. Ground lichen pastures in the Muonio herding district are mostly heavily grazed (lichen biomass < 300 kg ha-1) although the lichen biomass is higher in a winter range than in a summer range area (Kumpula et al., 2009). Arboreal lichen is found most abundantly in the old growth pine and spruce forests. Intensive land use forms in the area are forest harvesting in commercial forest area, and tourism in more local fell areas. The largest allowed number of reindeer within the district during winter is 6000; the mean number of reindeer has been 5579±419 during years 2000-2007. Historical records and reindeer data Reindeer herders’ observations and experiences of winters were collected from the annual management reports during winters 1972/19732009/2010. Additionally, reindeer census data from the Muonio district consisting of the numbers of reindeer counted during the annual round-ups in the autumn/early winter slaughter season during the period 1972-2010 was used. Annual calf production percent in the slaughter season (autumn/early winter) after each winter was produced using information on number of calves per 100 female reindeer (calf production percentage, CPP) (data provided by Reindeer Herders’ Association). Meteorological data A 37-year time series of winter weather conditions (1972-2010, except winter 1982/1983; from 1 October to 30 April for each winter) was available from a synoptic observation station in Muonio, operated by Finnish Meteorological Institute (Fig. 1). The following weather parameters were obtained: air temperature (°C), relative humidity (%), wind velocity (m s-1) and wind direction (°), all observed from 2 meter height above the ground level. In addition, daily precipitation (mm) and snow depth values (m) were available from the station. Rangifer, 34, (1) 2014 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 32 (1), 2012 40 Annual mean temperature measured in the Muonio meteorological station was -1.4 °C during years 1971-2000, and annual precipitation 484 mm. Mean annual maximum snow depth during the period was 81 cm, with permanent snow cover usually formed after mid-October and with melting during May. Maximum snow depth is normally measured in March. (Drebs et al., 2002) We assume that weather conditions observed at the Muonio FMI station represent relatively well the general conditions of the whole reindeer herding district, and that the between-year variability observed at the Muonio station can be used as an estimate of the between-year variability on a larger area around the station. The SNOWPACK model The meteorological observations were used to run the SNOWPACK-model. SNOWPACK is a one dimensional model for snowpack mass and energy balance, developed by the Swiss Federal Institute for Snow and Avalanche Research (SLF). A complete description of the model can be found in Bartelt and Lehning (2002) and Lehning et al. (2002a; 2002b). As a physically based model, SNOWPACK has been used in several applications, e.g. in avalanche forecasting (Lehning & Fierz, 2008) and as a part of watershed scale hydrological modeling (Lehning et al., 2006). SNOWPACK can estimate the evolution of the layered structure in the snow cover and the physical properties of these layers (grain size, grain form and bonding between the grains, temperature, density and hardness of snow, fractions of ice, liquid water and air volume in snow). It has been used together with a regional climate model by inputting the climate model output data when future changes in snow cover in open area were evaluated during a 100 year time scale in the selected locations in Finland (Rasmus et al., 2004) and more recently when future snow cover and its runoff in the Alps were simulated (Bavay et al., 2009). The ability of the model to simulate the snow mass balance and snow structural properties has been validated in several climate conditions (Lehning et al., 1998; Lundy et al., 2001; Rasmus et al., 2007) and it has proven to be reliable, especially in open areas. In snow structure simulations, snow temperature and density had highest correlations with observations (r=0.90 and 0.85, respectively) and grain size and type lower (r=0.30; contingency coefficient C=0.71) (Lundy et al., 2001). SNOWPACK uses air temperature, relative humidity, wind velocity and wind direction, and incoming shortwave and longwave radiation with 0.5-6 hour temporal resolution as input data. Depending on data and the aim of the simulations, either observed snow depth or precipitation can be used in the mass balance calculations of the model. Use of snow depth is justified when the data is easily available and when it is more important to simulate the snow layer properties most reliably, and in the open areas. However, precipitation data is still needed to correctly simulate the rain events which lead to icy layer formation in the snow cover. Model simulations on snow structure evolution The SNOWPACK-model was used to produce a 37-year time series on the annual evolution of snow structure on the basis of the used weather input data. Recently a canopy module has been added to the SNOWPACK model, which allows simulations also below the forest canopies (Lehning et al., 2006). The canopy radiation transmission sub-model has been calibrated and evaluated by Stähli et al. (2009), but the ability of SNOWPACK to correctly simulate the snow structure below the canopies has yet to be validated. Additionally, the energy and mass balance calculations below the canopies are sensitive to correct estimates of forest parameters (forest height, LAI and sky view fraction; Rasmus et al., 2012). For these reasons we decided to run our simulations in open area Rangifer, 34, (1) 2014 32 (1), 2012 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no conditions only as meteorological input data was only available for open areas. Temperature, humidity and wind data were obtained from the Muonio FMI station with a three hours resolution. Incoming shortwave radiation (W m-2) was available from the Sodankylä FMI station (approximately 170 km away) with the same temporal resolution. Incoming longwave radiation (W m-2) was estimated using the difference between potential and observed incoming shortwave radiation, air temperature and relative humidity in each time step (method described in Konzelmann et al., 1994). Daily snow depth observations from the Muonio FMI station were used as a given parameter in simulations, because it is assumed that more exact the snow depth, the better the quality of the structure simulations. As a bottom boundary condition there is a standard soil assumed (Bartelt & Lehning, 2002) as well as a prescribed temperature profile in the beginning of the runs. Simulations were started on 1 October and finished on 30 April for each winter. Model output included time series for the mass and energy balance components in the snow cover, as well as graphical and numerical time series of the snow structure. Validation of the snow density simulations In this study the model SNOWPACK was used to simulate the snow structure, not depth or duration of the snow cover. Grain type and bonding between the grains largely determine the density of the snow, so density simulations are suitable for testing the performance of the model. For the validation of the snow density simulations made by SNOWPACK, we used the monthly mean snow density values measured in four permanent snow survey lines located around the Muonio weather station (Fig. 1). These long-term snow survey lines are operated by Finnish Environmental Institute, SYKE. Lines are four kilometres long with 80 snow depth and eight to ten snow density measurements, designed to include the typical terrain and biotypes (open areas, forest openings, bogs and different forest types) of the region. (Perälä & Reuna, 1990) Calculations Parameters from both meteorological observations as well as from simulation outputs were listed in each winter (Table 1). From simulation outputs the average values of parameters were calculated for the whole winter period (November-April) and for three winter periods separately - early winter (November-December), mid-winter (January-February) and late winter (March-April). If snow fell later than 1 November or melted before 30 April, the average values for each parameter have been 41 Parameter Unit From meteorological observations: Mean snow depth m Maximum snow depth m Snow cover formation date Snow melt date Snow cover duration days From simulation outputs: Ground surface temperature on snow formation date °C Mean ground surface temperature °C Mean thickness of icy layers cm Mean fraction of icy layers of the total snow depth 0-1 Mean thickness of ground ice cm Mean hardness N Mean bottom layer hardness N Mean density kg m-3 Mean thickness of layers with density > 350 kg m-3 cm Table 1. Parameters listed from meteorological observations and from simulation outputs in each of the study winters. Snow density above a 350 kg m-3 threshold was considered as icy and problematic for reindeer grazing (Vikhamar-Schuler et al., 2013). Rangifer, 34, (1) 2014 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 32 (1), 2012 Figure 1. The reindeer management area and its 56 herding districts in northern Finland (the Muonio reindeer herding district shaded). Locations of the meteorological observation station operated by Finnish Meteorological Institute (FMI) in Alamuonio and the four Finnish Environment Institute’s snow measurement lines (Hetta, Hormakumpu, Kattilamaa and Pulju) are marked on the map. 42 Rangifer, 34, (1) 2014 32 (1), 2012 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 43 calculated for the snow covered period only. A layer was classified as icy if simulation indicated melt and refreeze of the layer, and either major or minor grain type of the layer was melt/refrozen grains. A bottom layer was classified as ground ice if both major and minor grain types were melt/refrozen grains, and layer had gone through melt and refreeze. Statistical analysis of the reindeer and snow data was done using the Systat 13 and the IBM SPSS Statistics 20 softwares. Trends and statistical significance of the observed trends in reindeer and snow data were examined using the Mann-Kendall test. Pearson correlation test was conducted between the studied snow related parameters. The unpaired two sample t-test was used to determine the differences in snow parameters between the winters judged as difficult or easy according the reindeer herders. T-tests were done as two-tailed and assuming equal variance for the two samples. A principal component analysis (PCA) was made to extract the components accounting for most of the variance in our set of 14 observed or simulated snow related parameters. Extracted four principal components were included in the analyses of correlations and t-tests. Results Snow characteristics relevant for reindeer herding Reindeer herders’ experiences The annual management reports of the Muonio herding district include, among other information, reindeer herders’ experiences of snow conCPP Type of snow condition Impacts / reactions 1972/1973 36.9 Mold growth on pastures; Late melt Winter mortality 1976/1977 31.2 Late melt 1979/1980 50.8 Deep snow; Late melt Difficulties in grazing 1990/1991 48.7 Deep snow Difficulties in grazing 1991/1992 52.4 Snow to unfrozen ground; Ground ice Difficulties in grazing; Active movement of reindeer; Winter mortality 1992/1993 28.7 Deep snow Difficulties in grazing; Feeding; Winter mortality 1993/1994 29.9 Late melt Difficulties in grazing 1994/1995 49.0 Deep snow; Late melt Difficulties in grazing 1995/1996 26.4 Deep snow; Late melt Difficulties in grazing; Winter mortality 1996/1997 20.6 Deep snow; Mold growth on pastures Difficulties in grazing; Feeding 1997/1998 49.6 Deep snow Difficulties in grazing; Feeding 2004/2005 68.4 Deep snow; Ice layers Difficulties in grazing; Feeding 2006/2007 56.5 Deep snow; Ground ice Difficulties in grazing; Feeding; Winter mortality 2007/2008 54.3 Deep snow; Late melt Difficulties in grazing 2008/2009 52.3 Deep snow; Late melt Difficulties in grazing; Feeding 2009/2010 58.6 Deep snow; Late melt; Ground ice Difficulties in grazing; Feeding Table 2. Difficult snow conditions informed in the annual management reports of the Muonio reindeer herding district during 1972-2010. Calf production percentage (CPP), type of snow condition and reported impacts of snow conditions on reindeer populations (difficulties in grazing/active movement of reindeer/winter mortality) as well as responses of reindeer herding practices (feeding) are listed. Rangifer, 34, (1) 2014 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 32 (1), 2012 44 ditions during the winters and their response to difficult snow conditions. We had access to 38 reports between 1972/1973-2009/2010. Altogether 22 of the winters were classed as easy; in 16 winters snow conditions were experienced difficult (Table 2) and 12 of these cases were explained by deep snow cover. During nine of the winters snow melted late. In four winters, problems were caused by icy snow or ground ice (1991/1992, 2004/2005, 2006/2007 and 2009/2010). During three autumns the snow cover was reported to be formed on unfrozen ground, which means favorable conditions for mold growth and mold growth on pastures was reported during two of these winters. According the t-test, mean and maximum snow depth as well as, consequently, ground surface temperature were significantly higher (p<0.001) during the winters with reported difficult snow conditions; and snow season was significantly longer (P=0.02). Observed snow conditions and reindeer calf production Snow depth and length of snow cover time varied greatly among the winters (Table 3). No significant trends were observed in the long time series of these. Large between-year variability was seen also in calf production percentage during the observation period (Fig. 2). Weak but statistically significant increase in CPP of 0.483 per year was estimated using the Mann-Kendall test on trend in a time series (P=0.002). Relevance of snow depth and melt date, experienced by reindeer herders, was confirmed since CPP was negatively correlated to winter mean and maximum snow depth (R=-0.45; P=0.005 and -0.38; 0.02, respectively) and length of snow cover time (R=- 0.37; P=0.02) (Fig. 3). Still, winters with reported difficult snow conditions did not clearly show in the time series of CPP (Fig. 2) and Mean Min Max St. Dev. Formation date 24.10 3.10 27.11 12 days Melt date 14.5 28.4 1.6 8 days Duration (days) 202 166 229 16 Max snow depth (cm) 82 55 109 15 Table 3. Mean, minimum, maximum and standard deviation of snow amount and duration parameters in Muonio during 1972/19732009/2010. Figure 2. The annual and mean calf production percentage (CPP) in the Muonio reindeer herding district during 1972-2010. Winters experienced as difficult by the reindeer herders are marked with stars. Rangifer, 34, (1) 2014 32 (1), 2012 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 45 CPP between winters with easy and difficult snow conditions did no differ significantly from each other according the t-test. Validation of the model SNOWPACK Simulated values of mean monthly snow densities were compared to the monthly observations from the four survey lines of Finnish Environment Institute (Fig. 4). The densities simulated by the SNOWPACK were generally higher than the observed ones; however, inter-annual variation in snow density was well reproduced by the model. The Pearson correlation coefficients between the SNOWPACK model outputs and the snow survey observations ranged from 0.08 in Hormakumpu (P=0.745), 0.49 in Kattilamaa (P=0.002), 0.56 in Hetta (P=0.001) to 0.58 in Pulju (P=0.004). When mean value of these four surveys was compared Figure 3. Calf production percentage (CPP) in relation to the annual observed maximum snow depth (a) and duration of the snow cover (b) in the Muonio reindeer herding district during 1972-2010. Pearson correlation coefficients and P-values given in the figures. Figure 4. The mean snow density values (calculated from monthly values for whole winter) in open areas at four Finnish Environment Institute’s snow measurement lines and in the SNOWPACK simulations for open area. 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Manuscript submitted 2 October 2013 revision accepted 10 February 2014 54 Rangifer, 34, (1) 2014 32 (1), 2012 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no Onko lumipeitteen rakenteen mallilla mahdollista arvioida poronhoidolle merkityksellisiä lumen ominaisuuksia? Summary in Finnish/Tiivistelmä: Lumi vaikuttaa porojen laidunnusolosuhteisiin esimerkiksi lisäämällä liikkumisen ja kaivamisen energiankulutusta tai helpottamalla luppojäkälän saatavuutta. Tutkimuksia lumen rakenteen vaikutuksista porojen populaatiodynamiikkaan tai poronhoitoon on kuitenkin tehty vähän. Tutkimuksemme tavoitteena oli selvittää mitkä lumen ominaisuudet ovat poronhoitajien kokemusten mukaan merkityksellisiä poroille pohjoisboreaalisella vyöhykkeellä, ja vaikuttavatko nämä myös alueen porojen lisääntymismenestykseen. Tavoitteenamme oli myös tutkia kykeneekö lumen rakenteen SNOWPACK-malli luotettavasti arvioimaan nämä lumen ominaisuudet. Yhdistimme työssämme Muonion paliskunnassa, pohjoisessa Suomessa, tehtyjä meteorologisia havaintoja ja lumen rakenteen simulointeja sekä paliskunnan poronhoitajien vuosiraportteja vuosilta 1972-2010. Syvä lumi ja myöhäinen lumen sulaminen olivat yleisimmät raportoidut epäsuotuisat lumiolot. Lumen rakenteeseen liittyneet vaikeat olot tarkoittivat jäisiä lumikerroksia, maajäätä tai sulaa maata lumipeitteen alla, joka johti homeiden kasvuun laitumilla. Havaitsimme käänteisen riippuvuuden vasaprosentin sekä talven suurimman lumensyvyyden, lumipeiteajan keston ja lumen tiheyden välillä. SNOWPACK –malli kykenee suhteellisen luotettavasti arvioimaan poroille merkityksellisiä lumen rakenteellisia ominaisuuksia. Mallisimulaatioiden avulla erotimme kolme neljästä sellaisesta talvesta, joina poronhoitajat raportoivat vaikeista lumiolosuhteista jäisen lumen tai maajään vuoksi. Pystyimme myös luotettavasti erottamaan talvet, joiden olosuhteet mahdollistivat homeiden kasvun laitumille. Lumen rakenteen malli voi antaa arvokasta tietoa laidunnusolosuhteista, etenkin kun tarkastellaan lämpenevien talvien mahdollisia vaikutuksia poropopulaatioihin ja poronhoitoon elinkeinona. 55 Rangifer, 34, (1) 2014 This journal is published under the terms of the Creative Commons Attribution 3.0 Unported License Editor in Chief: Birgitta Åhman, Technical Editor Eva Wiklund and Graphic Design: Bertil Larsson, www.rangifer.no 32 (1), 2012 56