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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.
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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
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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:
8IJDIPGUIFTOPXDIBSBDUFSJTUJDTBSFSFM-
e an o eindee he ding in no he n bo eal
zone acco ding o he expe iences o eindee
he de s?
*TUIJTSFMFWBODFTFFOBMTPJOSFQSPEVDUJPO a e o eindee in his a ea?
*TJUQPTTJCMFUPVTFUIF4/081"$,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?
%PFTBTOPXNPEFMBEEJOGPSNBUJPOPOTOPXand 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.
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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
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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).
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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
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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.
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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.
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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.
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32 (1), 2012
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54
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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
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56
