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

Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no Rangi e , 34, (1), 2014: 37-56 37 In oduc ion Semi-domes ica ed eindee (Rangi e a andus a andus) in no he n Finland li e in an en i- onmen whe e con inuously changing wea he and o aging condi ions signi ican ly a ec popula ions. In pa icula , eindee he ds mus o age o ood benea h he snow o six (sou h- e n he ds) o eigh (no he n he ds) mon hs a yea (Solan ie e al., 1996), wi h especially ju- enile su i al highly dependen on adequa e Can a snow s uc u e model es ima e snow cha ac e is ics ele an o eindee husband y? Si pa Rasmus1,2, Jouko Kumpula3 & Jukka Sii a i3 1 Depa men o Biological and En i onmen al Sciences, P.O. Box 35 (Su on ie 9), 40014 Uni e si y o Jy äskylä, Finland (Co esponding au ho : [email p o ec ed]). 2 Finnish Game and Fishe ies Resea ch Ins i u e, Jy äskylä Uni , Su on ie 9, 40500 Jy äskylä, Finland. 3 Finnish Game and Fishe ies Resea ch Ins i u e, Reindee Resea ch Uni , Toi oniemen ie 246, 99910 Kaamanen, Finland. Abs ac : Snow a ec s o aging condi ions o eindee e.g. by inc easing he ene gy expendi u es o mo ing and digging wo k o , in con as , by making access o a bo eal lichen easie . S ill he s udies concen a ing on he ole o he snow pack s uc u e on eindee popula ion dynamics and eindee managemen a e ew. We aim o ind ou which o he snow cha ac e is ics a e ele an o eindee in he no he n bo eal zone acco ding o he expe iences o eindee he d- e s and is his ele ance seen also in ep oduc ion a e o eindee in his a ea. We also aim o alida e he abili y o he snow model SNOWPACK o eliably es ima e he ele an snow s uc u e cha ac e is ics. We combined me eo ological obse a ions, snow s uc u e simula ions by he model SNOWPACK and annual epo s by eindee he de s du ing win e s 1972-2010 in he Muonio eindee he ding dis ic , no he n Finland. Deep snow co e and la e snow mel we e he mos common un a o able condi ions epo ed. P oblema ic condi ions ela ed o snow s uc u e we e icy snow and g ound ice o un ozen g ound below he snow, leading o mold g ow h on g ound ege a ion. Cal p oduc- ion pe cen age was nega i ely co ela ed o he measu ed annual snow dep h and leng h o he snow co e ime and o he simula ed snow densi y. Win e s wi h icy snow could be dis inguished in h ee ou o ou epo ed cases by SNOW- PACK simula ions and we could de ec eliably win e s wi h condi ions a o able o mold g ow h. Bo h snow amoun and also quali y a ec s he eindee he ding and eindee ep oduc ion a e in no he n Finland. Model SNOWPACK can ela i ely eliably es ima e he ele an s uc u al p ope ies o snow. Use o snow s uc u e models could gi e aluable in o ma ion abou g azing condi ions, especially when es ima ing he possible e ec s o wa ming win e s on eindee popula ions and eindee husband y. Simila e ec s will be expe ienced also by o he a c ic and bo eal species. Key wo ds: cal p oduc ion; eindee ; Rangi e a andus a andus; snow; snow s uc u e; snow modeling. Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 38 win e o age (Holleman e al., 1979). This in u n is a ec ed bo h by he amoun o he main win e o age, ( eindee lichens Cladina spp.), and also by he snow condi ions on pas- u es (Skogland, 1978; Helle & Ta ainen, 1984; Kumpula, 2001). Bo h eindee and i s no he n Ame ican ela i e, ca ibou (Rangi e a andus), a e mo phologically and beha io al- ly adap ed o A c ic ecosys ems (Tel e & Ken- sall, 1984). Reindee he de s acknowledge he e ec s o wea he and snow condi ions on well- being o hei he ds, and husband y has always been ela i ely adap able o wha comes o in- a- and in e -annual a ia ions in g azing con- di ions (Tyle e al., 2007; Ro u ie & Roue, 2009; Rise h e al., 2010; Vuojala-Magga e al., 2011). Despi e his, he deep snow co e and la e snow mel in sp ing can cause high win e mo ali y (Adamczewski e al., 1988; Kumpula & Colpae , 2003; Helle & Kojola, 2008) and low cal p oduc ion (Adams & Dale 1998; Pos & S ense h, 1999; Aanes e al., 2000; Kumpu- la, 2001) o bo h ca ibou and eindee . In addi ion o amoun o snow, he s uc- u al p ope ies o snow a e also impo an . The ene gy equi ed o digging e o is g ea - e wi h inc easing snow densi y and ha dness (Fancy & Whi e, 1985; Kumpula e al., 2004). Ex ensi e g ound ice (due o hawing- eezing a he snow-g ound in e ace) has been ob- se ed o dec ease he ep oduc ion a es o S alba d eindee (Rangi e a andus pla y hyn- chus) popula ion (Hansen e al., 2011) o e en cause popula ion c ashes (Helle, 1980; Kohle & Aanes, 2004). In addi ion, he numbe o wa m days (mean T > 0 °C) du ing ea ly win- e o he win e ime ain e en s, which is as- sumed o lead o dense o icy snow co e ha e been shown o dec ease he cal p oduc ion and win e su i al o eindee (Lee e al., 2000; Solbe g e al., 2001; Kumpula & Colpae , 2003; Helle & Kojola, 2008). Damages o ein- dee by p eda ion a e pa ly connec ed o snow condi ions. Majo i y o p e ious esea ch has been based on measu emen s on snow dep h and me eo o- logical obse a ions ha ha e daily o oughe ime scales. I is di icul o iden i y win e s wi h icy snow co e using his kind o obse a- ions only (Helle & Kojola, 2008; Vikhama - Schule e al., 2013). In Vikhama -Schule e al. (2013), a snow s uc u e model SNOWPACK was success ully used o simula e he e olu ion o he snow co e , especially high-densi y lay- e s, du ing yea s 1956-2010 in Kau okeino (Guo dageaidnu), No he n No way. SNOWPACK (Ba el & Lehning, 2002; Lehning e al., 2002a and 2002b) is a widely used model o desc ibing he de elopmen o snow mass and ene gy balance du ing he win- e . I is one o he ew exis ing snow s uc u e models and enables o es ima e he laye ed s uc u e wi hin he snow co e and physical p ope ies (e.g. densi y, ha dness, g ain size, g ain ype and bonding be ween he g ains) o he laye s. In his wo k we used combina ion o de ailed me eo ological in o ma ion, snow s uc u e simula ions by he model SNOW- PACK and he annually made eindee he de s’ epo s o c ea e a comp ehensi e iew on snow condi ions in a selec ed eindee he ding dis- ic in Muonio, no he n Finland. Due o an in ensi e managemen sys em ela i ely eliable es ima es on annual mo al- i y and p oduc i i y o Scandina ian eindee popula ion a e a ailable. Also win e condi- ions, including di icul snow condi ion, a e annually epo ed by eindee he de s. Un a- ou able snow and wea he condi ions a ec in a simila way o o he no he n ungula es, and mo e b oadly, o se e al a c ic and bo eal spe- cies. The global mean empe a u e is p edic ed o inc ease by 1.4 – 6.4 °C by he end o he yea 2100 (IPCC, 2007). This wa ming will mos likely be mos ex eme du ing win e s in no h-eas e n Eu ope, and p ecipi a ion (con- sis ing o ain on snow du ing wa m win e s) is expec ed o inc ease. These changes will al e Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 39 he amoun and s uc u e o snow co e , as well as in he leng h o he snow season, in many loca ions (Venäläinen e al., 2001; Räisänen, e al., 2003; ACIA, 2004; Rasmus e al., 2004; Kellomäki e al., 2010). Used oge he wi h clima e model ou pu da a, SNOWPACK can wo k as a ool in clima e impac s udies. The e- o e, i is impo an o alida e his modelling ool in p esen day condi ions and o examine i s de elopmen needs. We aim o answe he ollowing ques ions: 8IJDIPGUIFTOPXDIBSBDUFSJTUJDTBSFSFM- e an o eindee he ding in no he n bo eal zone acco ding o he expe iences o eindee he de s? *TUIJTSFMFWBODFTFFOBMTPJOSFQSPEVDUJPO a e o eindee in his a ea? *TJUQPTTJCMFUPVTFUIF4/081"$,NPEFM o eliably es ima e he ele an snow s uc u e cha ac e is ics wi hin he s udy a ea? %PFTBTOPXNPEFMBEEJOGPSNBUJPOPOTOPXand o aging condi ions by eindee compa ed o he con en ional me eo ological obse a- ions? Ma e ials and me hods S udy a ea The Muonio eindee he ding dis ic (2670 km2) is loca ed in he no he n bo eal zone ep esen ing ypical he ding dis ic s in middle pa s o Finnish Lapland (Fig. 1). Snow condi- ions a e a he homogenous h ough he dis- ic . Reindee a e mainly g azed on he na u al pas u es in Muonio, e en hough supplemen- a y win e eeding has g adually inc eased. Acco ding o he eindee pas u e in en o y conduc ed du ing 2005–2008, 27.5% o he land a ea is co e ed by g ound lichen pas u es, 38.7% by ma u e and old coni e ous o es s wi h a bo eal lichen, 20.1% by dwa sh ub and g aminoid ege a ion and 27.5% by mi es (Kumpula e al., 2009). Only small ac ion o he land a ea is high ele a ion (>300 m.a.s.l), und a ege a ion. G ound lichen pas u es in he Muonio he ding dis ic a e mos ly hea ily g azed (lichen biomass < 300 kg ha-1) al hough he lichen biomass is highe in a win e ange han in a summe ange a ea (Kumpula e al., 2009). A bo eal lichen is ound mos abundan - ly in he old g ow h pine and sp uce o es s. In ensi e land use o ms in he a ea a e o es ha es ing in comme cial o es a ea, and ou - ism in mo e local ell a eas. The la ges allowed numbe o eindee wi hin he dis ic du ing win e is 6000; he mean numbe o eindee has been 5579±419 du ing yea s 2000-2007. His o ical eco ds and eindee da a Reindee he de s’ obse a ions and expe iences o win e s we e collec ed om he annual man- agemen epo s du ing win e s 1972/1973- 2009/2010. Addi ionally, eindee census da a om he Muonio dis ic consis ing o he numbe s o eindee coun ed du ing he annual ound-ups in he au umn/ea ly win e slaugh e season du ing he pe iod 1972-2010 was used. Annual cal p oduc ion pe cen in he slaugh e season (au umn/ea ly win e ) a e each win e was p oduced using in o ma ion on numbe o cal es pe 100 emale eindee (cal p oduc ion pe cen age, CPP) (da a p o ided by Reindee He de s’ Associa ion). Me eo ological da a A 37-yea ime se ies o win e wea he condi- ions (1972-2010, excep win e 1982/1983; om 1 Oc obe o 30 Ap il o each win e ) was a ailable om a synop ic obse a ion s a- ion in Muonio, ope a ed by Finnish Me eo o- logical Ins i u e (Fig. 1). The ollowing wea he pa ame e s we e ob ained: ai empe a u e (°C), ela i e humidi y (%), wind eloci y (m s-1) and wind di ec ion (°), all obse ed om 2 me e heigh abo e he g ound le el. In addi- ion, daily p ecipi a ion (mm) and snow dep h alues (m) we e a ailable om he s a ion. Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 40 Annual mean empe a u e measu ed in he Muonio me eo ological s a ion was -1.4 °C du - ing yea s 1971-2000, and annual p ecipi a ion 484 mm. Mean annual maximum snow dep h du ing he pe iod was 81 cm, wi h pe manen snow co e usually o med a e mid-Oc obe and wi h mel ing du ing May. Maximum snow dep h is no mally measu ed in Ma ch. (D ebs e al., 2002) We assume ha wea he condi ions obse ed a he Muonio FMI s a ion ep esen ela i ely well he gene al condi ions o he whole ein- dee he ding dis ic , and ha he be ween-yea a iabili y obse ed a he Muonio s a ion can be used as an es ima e o he be ween-yea a i- abili y on a la ge a ea a ound he s a ion. The SNOWPACK model The me eo ological obse a ions we e used o un he SNOWPACK-model. SNOWPACK is a one dimensional model o snowpack mass and ene gy balance, de eloped by he Swiss Fede al Ins i u e o Snow and A alanche Re- sea ch (SLF). A comple e desc ip ion o he model can be ound in Ba el and Lehning (2002) and Lehning e al. (2002a; 2002b). As a physically based model, SNOWPACK has been used in se e al applica ions, e.g. in a a- lanche o ecas ing (Lehning & Fie z, 2008) and as a pa o wa e shed scale hyd ological model- ing (Lehning e al., 2006). SNOWPACK can es ima e he e olu ion o he laye ed s uc u e in he snow co e and he physical p ope ies o hese laye s (g ain size, g ain o m and bonding be ween he g ains, empe a u e, densi y and ha dness o snow, ac ions o ice, liquid wa e and ai olume in snow). I has been used o- ge he wi h a egional clima e model by inpu - ing he clima e model ou pu da a when u u e changes in snow co e in open a ea we e e alu- a ed du ing a 100 yea ime scale in he selec ed loca ions in Finland (Rasmus e al., 2004) and mo e ecen ly when u u e snow co e and i s uno in he Alps we e simula ed (Ba ay e al., 2009). The abili y o he model o simula e he snow mass balance and snow s uc u al p ope - ies has been alida ed in se e al clima e condi- ions (Lehning e al., 1998; Lundy e al., 2001; Rasmus e al., 2007) and i has p o en o be eliable, especially in open a eas. In snow s uc- u e simula ions, snow empe a u e and den- si y had highes co ela ions wi h obse a ions ( =0.90 and 0.85, espec i ely) and g ain size and ype lowe ( =0.30; con ingency coe icien C=0.71) (Lundy e al., 2001). SNOWPACK uses ai empe a u e, ela i e humidi y, wind eloci y and wind di ec ion, and incoming sho wa e and longwa e adia- ion wi h 0.5-6 hou empo al esolu ion as inpu da a. Depending on da a and he aim o he simula ions, ei he obse ed snow dep h o p ecipi a ion can be used in he mass balance calcula ions o he model. Use o snow dep h is jus i ied when he da a is easily a ailable and when i is mo e impo an o simula e he snow laye p ope ies mos eliably, and in he open a eas. Howe e , p ecipi a ion da a is s ill need- ed o co ec ly simula e he ain e en s which lead o icy laye o ma ion in he snow co e . Model simula ions on snow s uc u e e olu ion The SNOWPACK-model was used o p oduce a 37-yea ime se ies on he annual e olu ion o snow s uc u e on he basis o he used wea he inpu da a. Recen ly a canopy module has been added o he SNOWPACK model, which al- lows simula ions also below he o es canopies (Lehning e al., 2006). The canopy adia ion ansmission sub-model has been calib a ed and e alua ed by S ähli e al. (2009), bu he abili y o SNOWPACK o co ec ly simula e he snow s uc u e below he canopies has ye o be alida ed. Addi ionally, he ene gy and mass balance calcula ions below he canopies a e sensi i e o co ec es ima es o o es pa- ame e s ( o es heigh , LAI and sky iew ac- ion; Rasmus e al., 2012). Fo hese easons we decided o un ou simula ions in open a ea Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no condi ions only as me eo ological inpu da a was only a ailable o open a eas. Tempe a u e, humidi y and wind da a we e ob ained om he Muonio FMI s a ion wi h a h ee hou s esolu ion. Incoming sho wa e adia ion (W m-2) was a ailable om he So- dankylä FMI s a ion (app oxima ely 170 km away) wi h he same empo al esolu ion. In- coming longwa e adia ion (W m-2) was es i- ma ed using he di e ence be ween po en ial and obse ed incoming sho wa e adia ion, ai empe a u e and ela i e humidi y in each ime s ep (me hod desc ibed in Konzelmann e al., 1994). Daily snow dep h obse a ions om he Muonio FMI s a ion we e used as a gi en pa ame e in simula ions, because i is assumed ha mo e exac he snow dep h, he be e he quali y o he s uc u e simula ions. As a bo om bounda y condi ion he e is a s anda d soil assumed (Ba el & Lehning, 2002) as well as a p esc ibed empe a u e p o ile in he beginning o he uns. Simula ions we e s a ed on 1 Oc obe and inished on 30 Ap il o each win e . Model ou pu included ime se ies o he mass and ene gy balance compo- nen s in he snow co e , as well as g aphical and nume ical ime se ies o he snow s uc u e. Valida ion o he snow densi y simula ions In his s udy he model SNOWPACK was used o simula e he snow s uc u e, no dep h o du a ion o he snow co e . G ain ype and bonding be ween he g ains la gely de e mine he densi y o he snow, so densi y simula ions a e sui able o es ing he pe o mance o he model. Fo he alida ion o he snow densi y sim- ula ions made by SNOWPACK, we used he mon hly mean snow densi y alues measu ed in ou pe manen snow su ey lines loca ed a ound he Muonio wea he s a ion (Fig. 1). These long- e m snow su ey lines a e ope a ed by Finnish En i onmen al Ins i u e, SYKE. Lines a e ou kilome es long wi h 80 snow dep h and eigh o en snow densi y measu e- men s, designed o include he ypical e ain and bio ypes (open a eas, o es openings, bogs and di e en o es ypes) o he egion. (Pe älä & Reuna, 1990) Calcula ions Pa ame e s om bo h me eo ological obse a- ions as well as om simula ion ou pu s we e lis ed in each win e (Table 1). F om simula- ion ou pu s he a e age alues o pa ame e s we e calcula ed o he whole win e pe iod (No embe -Ap il) and o h ee win e pe iods sepa a ely - ea ly win e (No embe -Decem- be ), mid-win e (Janua y-Feb ua y) and la e win e (Ma ch-Ap il). I snow ell la e han 1 No embe o mel ed be o e 30 Ap il, he a e age alues o each pa ame e ha e been 41 Pa ame e Uni F om me eo ological obse a ions: Mean snow dep h m Maximum snow dep h m Snow co e o ma ion da e Snow mel da e Snow co e du a ion days F om simula ion ou pu s: G ound su ace empe a u e on snow o ma ion da e °C Mean g ound su ace empe a u e °C Mean hickness o icy laye s cm Mean ac ion o icy laye s o he o al snow dep h 0-1 Mean hickness o g ound ice cm Mean ha dness N Mean bo om laye ha dness N Mean densi y kg m-3 Mean hickness o laye s wi h densi y > 350 kg m-3 cm Table 1. Pa ame e s lis ed om me eo ological obse a- ions and om simula ion ou pu s in each o he s udy win e s. Snow densi y abo e a 350 kg m-3 h eshold was conside ed as icy and p oblema ic o eindee g azing (Vikhama -Schule e al., 2013). Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 Figu e 1. The eindee managemen a ea and i s 56 he ding dis ic s in no he n Finland ( he Muo- nio eindee he ding dis ic shaded). Loca ions o he me eo ological obse a ion s a ion ope a ed by Finnish Me eo ological Ins i u e (FMI) in Alamuonio and he ou Finnish En i onmen Ins i u e’s snow measu emen lines (He a, Ho makumpu, Ka ilamaa and Pulju) a e ma ked on he map. 42 Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 43 calcula ed o he snow co e ed pe iod only. A laye was classi ied as icy i simula ion indica ed mel and e eeze o he laye , and ei he majo o mino g ain ype o he laye was mel / e- ozen g ains. A bo om laye was classi ied as g ound ice i bo h majo and mino g ain ypes we e mel / e ozen g ains, and laye had gone h ough mel and e eeze. S a is ical analysis o he eindee and snow da a was done using he Sys a 13 and he IBM SPSS S a is ics 20 so wa es. T ends and s a- is ical signi icance o he obse ed ends in eindee and snow da a we e examined using he Mann-Kendall es . Pea son co ela ion es was conduc ed be ween he s udied snow ela ed pa ame e s. The unpai ed wo sample - es was used o de e mine he di e ences in snow pa ame e s be ween he win e s judged as di icul o easy acco ding he eindee he de s. T- es s we e done as wo- ailed and assuming equal a iance o he wo samples. A p incipal componen analysis (PCA) was made o ex ac he componen s accoun ing o mos o he a iance in ou se o 14 obse ed o simula ed snow ela ed pa ame e s. Ex ac ed ou p inci- pal componen s we e included in he analyses o co ela ions and - es s. Resul s Snow cha ac e is ics ele an o eindee he ding Reindee he de s’ expe iences The annual managemen epo s o he Muonio he ding dis ic include, among o he in o ma- ion, eindee he de s’ expe iences o snow con- CPP Type o snow condi ion Impac s / eac ions 1972/1973 36.9 Mold g ow h on pas u es; La e mel Win e mo ali y 1976/1977 31.2 La e mel 1979/1980 50.8 Deep snow; La e mel Di icul ies in g azing 1990/1991 48.7 Deep snow Di icul ies in g azing 1991/1992 52.4 Snow o un ozen g ound; G ound ice Di icul ies in g azing; Ac i e mo emen o eindee ; Win e mo ali y 1992/1993 28.7 Deep snow Di icul ies in g azing; Feeding; Win e mo ali y 1993/1994 29.9 La e mel Di icul ies in g azing 1994/1995 49.0 Deep snow; La e mel Di icul ies in g azing 1995/1996 26.4 Deep snow; La e mel Di icul ies in g azing; Win e mo ali y 1996/1997 20.6 Deep snow; Mold g ow h on pas u es Di icul ies in g azing; Feeding 1997/1998 49.6 Deep snow Di icul ies in g azing; Feeding 2004/2005 68.4 Deep snow; Ice laye s Di icul ies in g azing; Feeding 2006/2007 56.5 Deep snow; G ound ice Di icul ies in g azing; Feeding; Win e mo ali y 2007/2008 54.3 Deep snow; La e mel Di icul ies in g azing 2008/2009 52.3 Deep snow; La e mel Di icul ies in g azing; Feeding 2009/2010 58.6 Deep snow; La e mel ; G ound ice Di icul ies in g azing; Feeding Table 2. Di icul snow condi ions in o med in he annual managemen epo s o he Muonio eindee he ding dis ic du ing 1972-2010. Cal p oduc ion pe cen age (CPP), ype o snow condi ion and e- po ed impac s o snow condi ions on eindee popula ions (di icul ies in g azing/ac i e mo emen o eindee /win e mo ali y) as well as esponses o eindee he ding p ac ices ( eeding) a e lis ed. Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 44 di ions du ing he win e s and hei esponse o di icul snow condi ions. We had access o 38 epo s be ween 1972/1973-2009/2010. Al o- ge he 22 o he win e s we e classed as easy; in 16 win e s snow condi ions we e expe ienced di icul (Table 2) and 12 o hese cases we e explained by deep snow co e . Du ing nine o he win e s snow mel ed la e. In ou win e s, p oblems we e caused by icy snow o g ound ice (1991/1992, 2004/2005, 2006/2007 and 2009/2010). Du ing h ee au umns he snow co e was epo ed o be o med on un ozen g ound, which means a o able condi ions o mold g ow h and mold g ow h on pas u es was epo ed du ing wo o hese win e s. Acco d- ing he - es , mean and maximum snow dep h as well as, consequen ly, g ound su ace em- pe a u e we e signi ican ly highe (p<0.001) du ing he win e s wi h epo ed di icul snow condi ions; and snow season was signi ican ly longe (P=0.02). Obse ed snow condi ions and eindee cal p o- duc ion Snow dep h and leng h o snow co e ime a - ied g ea ly among he win e s (Table 3). No sig- ni ican ends we e obse ed in he long ime se ies o hese. La ge be ween-yea a iabili y was seen also in cal p oduc ion pe cen age du - ing he obse a ion pe iod (Fig. 2). Weak bu s a is ically signi ican inc ease in CPP o 0.483 pe yea was es ima ed using he Mann-Kendall es on end in a ime se ies (P=0.002). Rele ance o snow dep h and mel da e, expe ienced by ein- dee he de s, was con i med since CPP was nega i ely co ela ed o win e mean and maximum snow dep h (R=-0.45; P=0.005 and -0.38; 0.02, espec i ely) and leng h o snow co e ime (R=- 0.37; P=0.02) (Fig. 3). S ill, win- e s wi h epo ed di icul snow condi ions did no clea ly show in he ime se ies o CPP (Fig. 2) and Mean Min Max S . De . Fo ma ion da e 24.10 3.10 27.11 12 days Mel da e 14.5 28.4 1.6 8 days Du a ion (days) 202 166 229 16 Max snow dep h (cm) 82 55 109 15 Table 3. Mean, minimum, maximum and s anda d de ia ion o snow amoun and du a ion pa ame e s in Muonio du ing 1972/1973- 2009/2010. Figu e 2. The annual and mean cal p oduc ion pe cen age (CPP) in he Muonio eindee he ding dis ic du ing 1972-2010. Win e s expe ienced as di icul by he eindee he de s a e ma ked wi h s a s. Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 45 CPP be ween win e s wi h easy and di icul snow condi ions did no di e signi ican ly om each o he acco ding he - es . Valida ion o he model SNOW- PACK Simula ed alues o mean mon h- ly snow densi ies we e compa ed o he mon hly obse a ions om he ou su ey lines o Finn- ish En i onmen Ins i u e (Fig. 4). The densi ies simula ed by he SNOWPACK we e gene ally highe han he obse ed ones; howe e , in e -annual a ia ion in snow densi y was well ep o- duced by he model. The Pea son co ela ion coe icien s be ween he SNOWPACK model ou pu s and he snow su ey obse a- ions anged om 0.08 in Ho - makumpu (P=0.745), 0.49 in Ka ilamaa (P=0.002), 0.56 in He a (P=0.001) o 0.58 in Pulju (P=0.004). When mean alue o hese ou su eys was compa ed Figu e 3. Cal p oduc ion pe cen age (CPP) in ela ion o he annual obse ed maximum snow dep h (a) and du a ion o he snow co e (b) in he Muonio eindee he ding dis ic du ing 1972-2010. Pea - son co ela ion coe icien s and P- alues gi en in he igu es. Figu e 4. The mean snow densi y alues (calcula ed om mon hly alues o whole win e ) in open a eas a ou Finnish En i onmen Ins i u e’s snow measu emen lines and in he SNOWPACK simula ions o open a ea. Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 and win e wea he . – Ecog aphy 31: 221- 230. Holleman, D. F., Luick, J. R. & Whi e, R. G. 1979. Lichen in ake es ima es o ein- dee and ca ibou du ing win e . – Jou nal o Wildli e Managemen 43: 192-201. Hols e , K. 1948. Suopunginhei oja. – Po o- mies 1948 (2): 21-23. IPCC. 2007. ‘Clima e Change 2007: The Physical Science Basis. Con ibu ion o Wo king G oup I o he Fou h Assessmen . Repo o he In e go e nmen al Panel on Clima e Change.’ [Ed. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Ma quis, K. B. A e y , M. Tigno and H. L. Mille ]. (Cam- b idge Uni e si y P ess, Camb idge and New Yo k.) Kellomäki, S., Maajä i, M., S andman, H., Kilpeläinen, A. & Pel ola, H. 2010. Model compu a ions on he clima e change e ec s on snow co e , soil mois u e and soil os in he bo eal condi ions o e Finland. – Sil a Fennica 44: 213-233. Kohle , J. & Aanes, R. 2004. E ec o win e snow and g ound-icing on a S alba d Rein- dee popula ion: Resul s o a simple snow- pack model. – A c ic, An a c ic and Alpine Resea ch 36: 333-341. Konzelmann, T., an de Wal, R.S.W., G euell, W., Bin anja, R. Henneken, E.A.C. & Abe- Ouchi, A. 1994. Pa ame e iza ion o global and longwa e incoming adia ion o he G eenland ice shee . – Global and Plane a y Change 9: 143-164. Kumpula, J., Pa ikka, P. & Nieminen, M. 2000. Occu ence o ce ain mic o ungi on eindee pas u es in no he n Finland du ing win e 1996-97. – Rangi e 20: 3-8. Kumpula, J. 2001. Win e g azing o eindee in woodland lichen pas u e – E ec o lichen a ailabili y on he condi ion o eindee . – Small Ruminan Resea ch 39: 121-130. Kumpula, J. & Colpae , A. 2003. E ec s o wea he and snow condi ions on ep o- duc ion and su i al o semi-domes ica ed eindee (R. . a andus). – Pola Resea ch 22: 225-233. Kumpula, J., Le è e, S. & Nieminen, M. 2004. The use o woodland lichen pas u e by eindee in win e wi h easy snow condi- ions. – A c ic 57: 273-278. Kumpula, J., Tanskanen, A., Colpae , A., An onen, M., Tö mänen, H., Sii a i, J. & Sii a i, S. 2009. Po onhoi oalueen poh- joisosan al ilai ume uosina 2005–2008: Laidun en ilan muu okse 1990-lu un puoli älin jälkeen. Riis a- ja kala alous: Tu - kimuksia No. 3. Finnish Game and Fishe ies Resea ch Ins i u e, Helsinki. Lee, S. E., P ess, M. C., Lee, J. A. Ingold, T. & Ku ila, T. 2000. Regional e ec s o cli- ma e change on eindee : A case s udy o he Muo ka un u i egion in Finnish Lapland. – Pola Resea ch 19: 99-105. Lehning, M., Ba el , P., B own, B., Russi, T., S öckli, U. & Zimme li, M. 1998. SNOW- PACK model calcula ions o a alanche wa ning based upon a ne wo k o wea he and snow s a ions. – Cold Regions Science and Technology 30: 145-157. Lehning, M., Ba el , P., B own, B., Fie z, C. & Sa yawali, P. 2002a. A physical SNOW- PACK model o he Swiss a alanche wa n- ing. Pa II. Snow mic os uc u e. – Cold Regions Science and Technology 35: 147-167. Lehning, M., Ba el , P., B own, B. & Fie z, C. 2002b. A physical SNOWPACK model o he Swiss a alanche wa ning se ice. Pa III. Me eo ological o cing, hin laye o ma- ion and e alua ion. – Cold Regions Science and Technology 35: 169-184 Lehning, M., Völksch, I., Gus a sson, D., Nguyen, T.A., S ähli, M. & Zappa, M. 2006. ALPINE3D: A de ailed model o moun ain su ace p ocesses and i s applica- ion o snow hyd ology. – Hyd ological P o- cesses 20: 2111-2128. Lehning, M. & Fie z, C. 2008. Assessmen o 52 Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no snow anspo in a alanche e ain. – Cold Regions Science and Technology 51: 240-252. Lundy, C., B own, R.L., Adams, E.E., Bi ke- land, K.W. & Lehning, M. 2001. A s a is- ical alida ion o he SNOWPACK model in a Mon ana clima e. – Cold Regions Science and Technology 33: 237-246. Moen, J. 2008. Clima e change: E ec s on he ecological basis o eindee husband y in Sweden. – Ambio 37: 304-311. Pe älä, J. & Reuna, M. 1990. Lumen esia - on alueellinen ja ajallinen aih elu Suomes- sa. Publica ions o Wa e and En i onmen Resea ch Ins i u e No. A 56. Wa e and En- i onmen Resea ch Ins i u e, Helsinki. Pos , E. & S ense h, N.C. 1999. Clima e change, plan phenology, and no he n un- gula es. – Ecology 80: 1322-1339. Pos , E. & Fo chhamme , M. 2002. Synch o- niza ion o animal popula ion dynamics by la ge-scale clima e. – Na u e 420: 168-171. Rasmus, S., Räisänen, J. & Lehning, M. 2004. Es ima ing snow condi ions in Fin- land in he la e 21s cen u y using he SNOWPACK-model wi h egional clima e scena io da a as inpu . – Annals o Glaciology 38: 238-244. Rasmus, S., G önholm, T., Lehning, M., Ras- mus, K. & Kulmala, M. 2007. Valida ion o he SNOWPACK-model in i e di e en snow zones in Finland. – Bo eal En i onmen- al Resea ch 12: 467-488. Rasmus, S., Gus a sson, D., Koi usalo, H., Lau én, A., G elle, A., Kauppinen, O.-K., Lang all, O., Lind o h, A., Rasmus, K., S ensson, M. & Weslien, P. 2012. Es ima- ion o win e lea a ea index and sky iew ac ion o snow modelling in bo eal coni - e ous o es s – Consequences on snow mass and ene gy balance. – Hyd ological P ocess- es, doi:10.1002/hyp.9432. Rise h J. Å., Tomme ik H., Helande -Ren- all E., Labba N., Johansson C., Malnes E., Bje ke J. W., Jonsson C., Pohjola V., Sa i L.-E., Schanche A. & Callaghan T.V. 2010. Sami adi ional ecological knowledge as a guide o science: snow, ice and eindee pas u e acing clima e change. – Pola Re- co d, doi:10.1017/S0032247410000434. Ro u ie , S. & Roue, M. 2009. O o es , snow and lichen: Sami eindee he de ’s knowledge o win e pas u es in no he n Sweden. – Fo es Ecology and Managemen 258: 1960-1967. Räisänen, J., Hansson, U., Ulle s ig, A., Dösche , R., G aham, L.P., Jones, C., Mei- e , M., Samuelsson, P. & Willén, U. 2003. GCM d i en simula ions o ecen and u- u e clima e wi h he Rossby Cen e coupled a mosphe e Bal ic Sea egional clima e mod- el RCAO. Repo s Me eo ology and Clima- ology No. 101, SMHI, No köping. Skogland, T. 1978. Cha ac e is ics o he snow co e and i s ela ionship o wild moun ain eindee (Rangi e a andus a andus L.) eed- ing s a egies. – A c ic and Alpine Resea ch 10: 569-580. Solan ie, R., D ebs, A., Hells en, E. & Sau- io, P. 1996. Lumipei een ulo-, läh ö- ja kes oajois a Suomessa al ina 1960/61- 1992/1993. Me eo ologisia julkaisuja No. 34. Finnish Me eo ological Ins i u e, Hel- sinki. Solbe g, E. J., Jo dhøy, P., S and, O.,. Aanes, R., Loison, A., Sæ he , B.-E. & Linnell, J.D.C. 2001. E ec s o densi y-dependence and clima e on he dynamics o a S alba d eindee popula ion. – Ecog aphy 24: 441- 451. S ähli, M., Jonas, T. & Gus a sson, D. 2009. The ole o snow in e cep ion in win e - ime adia ion p ocesses o a coni e ous sub-alpine o es . – Hyd ological P ocesses 23: 2498-2512. Tel e , E.S. & Kelsall, J.P. 1984. Adap a ion o some la ge no h Ame ican mammals o su i al in snow. – Ecology 65: 1828-1834. Tu unen, M., Soppela, P., Kinnunen, H., 53 Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 Su inen, M.-L. & Ma z, F. 2009. Does clima e change in luence he a ailabili y and quali y o eindee o age plan s? – Pola Bi- ology 32: 813-832. T e aa, T., Fauchald, P., Henaug, C. & Yoc- coz, N.G. 2003. An examina ion o a com- pensa o y ela ionship be ween ood limi- a ion and p eda ion in semi-domis ica ed eindee . – Oecologia 137: 370-376. Tyle , N., Tu i, J., Sundse , M., S øm Bull, K. Sa a, M., Reine , E., Oskal, N., Nel- lemann, C., McCa hy, J., Ma hiesen, S., Ma ello, M., Magga, O., Ho els ud, G., Hanssen-Baue , I., Ei a, N., Ei a, I. & Co ell, R. 2007. Saami eindee pas o alism unde clima e change: Applying a gene al- ized amewo k o ulne abili y s udies o a sub-a c ic social–ecological sys em. – Global En i onmen al Change 17: 191-206. Tyle , N J.C. 2010. Clima e, snow, ice, c ashes, and declines in popula ions o eindee and ca ibou (Rangi e a andus L.). – Ecological Monog aphs 80: 197-219. Venäläinen, A., Tuomen i a, H., Heikin- heimo, M., Kellomäki, S., Pel ola, H., S andman, H. & Väisänen, H. 2001. Im- pac o clima e change on soil os unde snow co e in a o es ed landscape. – Cli- ma e Resea ch 17: 63-72. Vikhama -Schule , D., Hanssen-Baue , I., Schule , T.V., Ma hiesen, S.D. & Lehning, M. 2013. Use o a mul i-laye snow model o assess g azing condi ions o eindee . – Annals o Glaciology 54 (62): 214-226. Vuojala-Magga T., Tu unen M., Ryyppö T. & Tennbe g M. 2011. Resonance s a egies o Sami eindee he ding du ing clima ically ex eme yea s in no he nmos Finland in 1970-2007. – A c ic 64: 227-241. Manusc ip submi ed 2 Oc obe 2013 e ision accep ed 10 Feb ua y 2014 54 Rangi e , 34, (1) 2014 32 (1), 2012 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no Onko lumipei een aken een mallilla mahdollis a a ioida po onhoidolle me ki yksellisiä lumen ominaisuuksia? Summa y in Finnish/Tii is elmä: Lumi aiku aa po ojen laidunnusolosuh eisiin esime kiksi lisäämällä liikkumisen ja kai amisen ene giankulu us a ai helpo amalla luppojäkälän saa a uu a. Tu kimuksia lumen aken een aiku uksis a po ojen populaa iodynamiikkaan ai po onhoi oon on kui enkin eh y ähän. Tu kimuksemme a oi eena oli sel i ää mi kä lumen ominaisuude o a po onhoi ajien kokemus en mukaan me ki yksellisiä po oille pohjoisbo eaalisella yöhyk- keellä, ja aiku a a ko nämä myös alueen po ojen lisään ymismenes ykseen. Ta oi eenamme oli myös u kia kykeneekö lumen aken een SNOWPACK-malli luo e a as i a ioimaan nämä lumen ominaisuude . Yhdis imme yössämme Muonion paliskunnassa, pohjoi- sessa Suomessa, eh yjä me eo ologisia ha ain oja ja lumen aken een simuloin eja sekä paliskun- nan po onhoi ajien uosi apo eja uosil a 1972-2010. Sy ä lumi ja myöhäinen lumen sulaminen oli a yleisimmä apo oidu epäsuo uisa lumio- lo . Lumen aken eeseen lii ynee aikea olo a koi i a jäisiä lumike oksia, maajää ä ai su- laa maa a lumipei een alla, joka joh i homeiden kas uun lai umilla. Ha ai simme kään eisen iippu uuden asap osen in sekä al en suu imman lumensy yyden, lumipei eajan kes on ja lu- men iheyden älillä. SNOWPACK –malli kykenee suh eellisen luo e a as i a ioimaan po oille me ki yksellisiä lumen aken eellisia ominaisuuksia. Mallisimulaa ioiden a ulla e o imme kolme neljäs ä sellaises a al es a, joina po onhoi aja apo oi a aikeis a lumiolosuh eis a jäisen lumen ai maajään uoksi. Pys yimme myös luo e a as i e o amaan al e , joiden olosuh ee mahdol- lis i a homeiden kas un lai umille. Lumen aken een malli oi an aa a okas a ie oa laidunnuso- losuh eis a, e enkin kun a kas ellaan lämpene ien al ien mahdollisia aiku uksia po opopulaa- ioihin ja po onhoi oon elinkeinona. 55 Rangi e , 34, (1) 2014 This jou nal is published unde he e ms o he C ea i e Commons A ibu ion 3.0 Unpo ed License Edi o in Chie : Bi gi a Åhman, Technical Edi o E a Wiklund and G aphic Design: Be il La sson, www. angi e .no 32 (1), 2012 56