Soil erosion by snow gliding

13 Pages·2014·3.94 MB·English

Hydrol.EarthSyst.Sci.,18,3763–3775,2014
www.hydrol-earth-syst-sci.net/18/3763/2014/
doi:10.5194/hess-18-3763-2014
©Author(s)2014.CCAttribution3.0License.
Soil erosion by snow gliding – a first quantification attempt
in a subalpine area in Switzerland
K.Meusburger1,G.Leitinger2,L.Mabit3,M.H.Mueller1,A.Walter1,andC.Alewell1
1EnvironmentalGeosciences,UniversityofBasel,Basel,Switzerland
2InstituteofEcology,UniversityofInnsbruck,Innsbruck,Austria
3SoilandWaterManagement&CropNutritionLaboratory,JointFAO/IAEADivisionofNuclearTechniquesinFoodand
Agriculture,InternationalAtomicEnergyAgency,Vienna,Austria
Correspondenceto:K.Meusburger([email protected])
Received:26February2014–PublishedinHydrol.EarthSyst.Sci.Discuss.:28March2014
Revised:28July2014–Accepted:7August2014–Published:26September2014
Abstract. Snow processes might be one important driver the constant influence of snow-glide-induced erosion, since
of soil erosion in Alpine grasslands and thus the unknown a large difference (lower proportion of water erosion com-
variable when erosion modelling is attempted. The aim of pared to total net erosion) was observed for sites with high
this study is to assess the importance of snow gliding as snow glide rates and vice versa. Moreover, the difference
a soil erosion agent for four different land use/land cover between RUSLE and 137Cs erosion rates was related to the
typesinasubalpineareainSwitzerland.Weusedthreedif- measuredsnowglidedistance(R2=0.64;p<0.005)andto
ferent approaches to estimate soil erosion rates: sediment the snow deposition sediment yields (R2=0.39; p=0.13).
yieldmeasurementsinsnowglidedepositions,thefalloutra- The SSGM reproduced the relative difference of the mea-
dionuclide 137Cs and modelling with the Revised Universal sured snow glide values under different land uses and land
Soil Loss Equation (RUSLE). RUSLE permits the evalua- cover types. The resulting map highlighted the relevance of
tion of soil loss by water erosion, the 137Cs method inte- snow gliding for large parts of the investigated area. Based
grates soil loss due to all erosion agents involved, and the on these results, we conclude that snow gliding appears to
measurement of snow glide deposition sediment yield can be a crucial and non-negligible process impacting soil ero-
be directly related to snow-glide-induced erosion. Further, sion patterns and magnitude in subalpine areas with similar
cumulative snow glide distance was measured for the sites topographicandclimaticconditions.
in the winter of 2009/2010 and modelled for the surround-
ing area and long-term average winter precipitation (1959–
2010)withthespatialsnowglidemodel(SSGM).Measured
1 Introduction
snow glide distance confirmed the presence of snow glid-
ing and ranged from 2 to 189cm, with lower values on the
Whilerainfallisawell-knownagentofsoilerosion,theero-
north-facing slopes. We observed a reduction of snow glide
sive forces of snow movements are qualitatively recognised
distancewithincreasingsurfaceroughnessofthevegetation,
butquantificationhasnotbeenachievedyet(Leitingeretal.,
whichisanimportantinformationwithrespecttoconserva-
2008; Konz et al., 2012). Wet avalanches, in particular, can
tion planning and expected and ongoing land use changes
yieldenormouserosiveforcesthatareresponsibleformajor
in the Alps. Snow glide erosion estimated from the snow
soil loss (Gardner, 1983; Ackroyd, 1987; Bell et al., 1990;
glide depositions was highly variable with values ranging
JomelliandBertran,2001;Heckmannetal.,2005;Fuchsand
from 0.03 to 22.9tha−1yr−1 in the winter of 2012/2013.
Keiler, 2008; Freppaz et al., 2010) in the avalanche release
For sites affected by snow glide deposition, a mean erosion
area(Ceaglioetal.,2012),too.
rateof8.4tha−1yr−1wasfound.Thedifferenceinlong-term
erosion rates determined with RUSLE and 137Cs confirms
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
3764 K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt
Besides avalanches, another important process of snow In contrast, the translocation of FRN reflects all erosion
movement affecting the soil surface is snow gliding processesbywater,windandsnowduringsummerandwin-
(InderGandandZupancic,1966).Snowglidingistheslow ter season and, thus, is an integrated estimate of the total
(millimetres to centimetres per day) downhill motion of a net soil redistribution rate since the time of the fallout in
snowpack over the ground surface caused by the stress of the1950s(thestartoftheglobalfalloutdeposit)and,inthe
its own weight (Parker, 2002). Snow gliding predominantly caseofpredominantChernobyl137Csinput,since1986.An-
occurs on south-east to south-west-facing slopes with slope thropogenicfalloutradionuclideshavebeenusedworldwide
anglesbetween30and40◦(InderGandandZupancic,1966; fordecadestoassessthemagnitudeofsoilerosionandsedi-
Leitinger et al., 2008). Two main factors that control snow mentationprocesses(MabitandBernard,2007;Mabitetal.,
glideratesare(i)thewetnessoftheboundarylayerbetween 2008; Matisoff and Whiting, 2011). The most well-known
thesnowandsoilcoverand(ii)thegroundsurfaceroughness conservative and validated anthropogenic radioisotope used
determinedbythevegetationcoverandrocks(McClungand to investigate soil redistribution and degradation is 137Cs
Clarke,1987;Neweselyetal.,2000).Sofar,onlyfewstudies (Mabitetal.,2013).
have investigated the effect of snow gliding on soil erosion For(sub-)alpineareasthedifferentsoilerosionprocesses
(Neweselyetal.,2000;Leitingeretal.,2008).Amajorrea- captured by RUSLE and the 137Cs method result in differ-
sonforthisshortcomingisthedifficultyinobtainingsoilero- enterosionrates(Konzetal.,2009;Juretzko,2010;Alewell
sionratescausedbysnowprocesses.Insteepsubalpineareas, et al., 2014; Stanchi et al., 2014). However, this difference
soil erosion records (e.g. with sediment traps) are restricted mightalsobeduetoseveralotherreasons,suchastheerror
to the vegetation period because avalanches and snow glid- ofbothapproaches,thenon-suitabilityoftheRUSLEmodel
ing can irreversibly damage the experimental design (Konz forthisspecificenvironmentand/ortheerroneousestimation
etal.,2012). oftheinitialfalloutof137Cs.
Recently,firstphysicallybasedattemptstomodeltheero- Inthisstudy,weaimtoquantifysnow-glide-inducedero-
sive force of wet avalanches were made (Confortola et al., sion and investigate whether the observed discrepancy be-
2012). No similar model exists for snow gliding. However, tweenerosionratesestimatedwithRUSLEandtheonespro-
thepotentialmaximumsnowglidedistanceduringatargeted videdbythe137Csmethodcanbeatleastpartlyattributedto
periodcanbemodelledwiththeempiricalspatialsnowglide snowglidingprocesses.Sincevegetationcoveraffectssnow
model(SSGM)(Leitingeretal.,2008).Themodellingofthis gliding, four different subalpine land use/land cover types
processiscrucialinevaluatingtheimpactofthesnowglide were investigated. A further objective of our research is to
processonsoilerosiononalargerscale. assess the relevance of snow gliding processes on a catch-
Soil erosion rates can be obtained by direct quantifica- mentscaleusingthespatialsnowglidemodel(SSGM).
tionofsedimenttransportinthefield,byfallout-radionuclide
(FRN)-based methods (e.g. Mabit et al., 1999; Benmansour
et al., 2013; Meusburger et al., 2013) and by soil erosion 2 Materialsandmethods
models (Nearing et al., 1989; Merritt et al., 2003). Since
the end of the 1970s empirical soil erosion models such as 2.1 Sitedescription
the Universal Soil Loss Equation (USLE; Wischmeier and
Smith, 1965, 1978) and its refined versions, the Revised ThestudysiteislocatedincentralSwitzerland(CantonUri)
USLE(RUSLE;Renardetal.,1997)andtheModifiedUSLE in the Ursern Valley (Fig. 1). The elevation of the W–E ex-
(MUSLE; Smith et al., 1984), have been used worldwide tended alpine valley ranges from 1400 up to 2500ma.s.l.
to evaluate soil erosion magnitude under various conditions Atthevalleybottom(1442ma.s.l.),averageannualairtem-
(Kinnell,2010).Thesewell-knownmodelsallowtheassess- peraturefortheyears1980–2012isaround4.1±0.7◦Cand
mentofsheeterosionandrill/inter-rillerosionundermoder- themeanannualprecipitationis1457±290mm,with30%
atetopography.However,theydonotintegrateerosionpro- fallingassnow(datafromMeteoSwiss).Thevalleyissnow-
cesses associated with wind, mass movement, tillage, chan- covered from November to April with a mean annual snow
nel or gully erosion (Risse et al., 1993; Mabit et al., 2002; heightof67cmintheperiod1980to2012.Drainageofthe
Kinnell,2005),andsnowimpactduetomovementisnotcon- basin is usually controlled by snowmelt from May to June.
sideredeither(Konzetal.,2009).Severalmodelshavebeen Animportantcontributiontotheflowregimetakesplacedur-
testedforsteepalpinesiteswiththeresultthatRUSLErepro- ing early autumn floods. Land use is characterised by hay-
ducedthemagnitudeofsoilerosion,therelativepatternand fields near the valley bottom (from 1450 to approximately
theeffectofthevegetationcovermostplausibly(Konzetal., 1650ma.s.l.) and pasturing further upslope. Siliceous slope
2010; Meusburger et al., 2010b; Panagos et al., 2014). The debris and moraine material is dominant at our sites, and
erosionratederivedfromRUSLEcorrespondstowaterero- forms Cambisols (Anthric) and Podzols (Anthric) classified
sioninducedbyrainfallandsurfacerunoffand,hence,inour accordingtoIUSSWorkingGroup(2006).
site,tothesoilerosionprocessesduringthesummerseason Of the 14 experimental sites, 9 are located on the south-
withoutsignificantinfluenceofsnowprocesses. facing slope and 5 on the north-facing slope at altitudes
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K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt 3765
Figure1.TheUrsernValleyinthecentralSwissAlpsandtheloca-
tionofthe14investigatedsites(hayfields–h;pastures–p;pastures
withdwarfshrubs–pw;andabandonedgrasslandcoveredwithAl-
nusviridis–A;north-facingslope–N).
between1476and1670ma.s.l.Fourdifferentlanduse/land
cover types with three to five replicates each were in-
vestigated: hayfields (h), pastures (p), pastures with dwarf
shrubs (pw) and abandoned grassland covered with Alnus Figure2.Illustrationoftheprocedureforsnow-glide-relatedero-
viridis(A).ThevegetationofhayfieldsisdominatedbyTri- sionrateassessment.
foliumpratense,Festucasp.,ThymusserpyllumandAgrostis
capillaris. For the pastured grassland, Globularia cordifo-
fieldcampaignperformedin2010,fiveadditionalsiteswere
lia, Festuca sp. and Thymus serpyllum dominate. Pastures
investigatedusingthesamemethodsforsoilerosionassess-
with dwarf shrubs are dominated by Calluna vulgaris, Vac-
mentwith137CsandRUSLEasin2007(Konzetal.,2009).
cinium myrtillus, Festuca violacea, Agrostis capillaris and
The 137Cs measurements were decay-corrected to 2007 for
Thymus serpyllum. At the pasture sites of the south-facing
comparisonpurposes.
slope,whicharestockedfromJunetoSeptember,cattletrails
transversetothemainslopedirection.
2.3.1 Snowandsedimentsamplinginthesnowglide
depositionarea
2.2 Snowglidemeasurement
Sediment concentrations were estimated by measuring the
We measured cumulative snow glide distances with snow
amountofsedimentinsnowsamplestakenwithacorerfrom
glideshoesforthewinterof2009/2010.Thesnowglideshoe
thesnowglidedepositionsinspring2013(Fig.2).Thecorer
equipment was similar to the set-up used by In der Gand
allowedforthesamplingoftheentiredepthofthesnowde-
andZupancic(1966),Neweselyetal.(2000)andLeitingeret
position and thus the integration of the sediment yield over
al.(2008).Theset-upconsistedofaglideshoeandaburied
the depth of the deposition. For larger depositions, samples
weather-proof box with a wire drum. Displacement of the
were collected along two transects across each deposition.
glide shoe causes the drum to unroll the wire. The total un-
Forsmallerdepositions,wetookthreesamples.Thesamples
rolled distance was measured in spring after snowmelt. To
weremeltedandfilteredthrougha0.11µmfilter.Thefiltered
prevententanglementwiththevegetation,thesteelwirewas materialwasdriedat40◦Candweightedtoobtainthecon-
protected by a flexible plastic tube. For each site, three to
centrationofsedimentpersample(M ).Themeansediment
s
fivesnowglideshoeswereinstalledtoobtainrepresentative
values(andfordepositionswithseveralsamplestheinterpo-
values.Atotalof60deviceswereused.
lated mean sedimentvalues) wereused toestimate thetotal
sediment load of the snow glide deposition (M ) according
A
2.3 Assessmentofsoilredistribution
to
Snowglidedistancewasmeasuredwithsnowglideshoesfor A ×M
M = A s, (1)
14 sites. For 12 of the 14 sites (exclusive of the two Alnus A
A
c
viridis sites on the north-facing slopes (AN)), RUSLE- and
137Cs-basederosionrateswereassessed.Sevenofthesesites where A is the area of the corer and A is the area of the
c A
weremeasuredin2007(Konzetal.,2009).Duringasecond snow glide deposition. The latter was mapped in the field
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3766 K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt
byGPSandmeasuringtape.Sedimentloadwasfurthercon- analyser1000,andgrainsizeanalyseswereperformedwith
vertedtosoilerosionrate(E)by sieves for grain sizes between 32 and 1000µm and with
a Sedigraph 5100 (Micromeritics) for grain sizes between
M
E = A, (2) 1and32 µm.LandS werecalculatedaccordingtoRenard
As etal.(1997).Thesupport-and-practicefactorP (dimension-
less) was set to 0.9 for some of the pasture sites because
whereA isthesourceareaofthesnowandsedimentdepo-
s alpinepastureswithcattletrailsresemblesmallterracestruc-
sition. Each snow glide was photo-documented and the re-
tures,which,itissuggested,areconsideredinP (Fosterand
spective source area was mapped with GPS and transferred
Highfill,1983).Forallothersites,thevalueofP wassetto1.
toArcGISforsurfaceareaestimation.
Thecover-and-managementfactorCwasassessedseparately
forsiteswithandwithoutdwarfshrubsusingmeasuredfrac-
2.3.2 Assessmentofsoilredistributionbywatererosion
tionalvegetationcover(FVC)inthefield.
usingtheRUSLE
For investigated sites without dwarf shrubs (US Depart-
The USLE (Wischmeier and Smith, 1978) and its revised ment of Agriculture, 1977), the C factor can be estimated
version the RUSLE (Renard et al., 1997) are empirical ero- with
sionmodelsoriginallydevelopedintheUS.Severaladapted
C = 0.45×e−0.0456×FVC, (4)
versions for other regions as well as for different temporal
resolutions have been developed and applied more or less
and for sites with dwarf shrubs, the following equation was
successfully(Kinnell,2010).Despiteitswell-knownlimita-
used:
tion (highlighted in our introduction), we selected RUSLE
becauseofthelackofsimplesoilerosionmodelsspecificfor C = 0.45×e−0.0324×FVC. (5)
mountain areas and, moreover, because of its better perfor-
mance when compared to the other existing models (Konz The FVC was determined in April and September using a
et al., 2010; Meusburger et al., 2010b). The RUSLE can be grid of 1m2 with a mesh width of 0.1m2. The visual esti-
calculatedusingthefollowingequation: mate of each mesh was averaged for the entire square me-
tre. This procedure was repeated four times for each plot.
A = R ×K ×LS×C ×P, (3) The maximum standard deviation was approximately 5%.
For the Alnus viridis sites, we used the value provided by
where A is the predicted average annual soil loss
the US Department of Agriculture (1977), i.e. 0.003. This
(tha−1yr−1).Ristherainfall–runoff-erosivityfactor(Nh−1)
value assumes a fall height of 0.5m and a ground cover of
that quantifies the effect of raindrop impact and reflects the
95–100%.
rateofrunofflikelytobeassociatedwiththerain(Renardet
The uncertainty assessment of the RUSLE estimates is
al., 1997). The soil erodibility factor K (kghN−1m−2) re- basedonthemeasurementerroroftheplotsteepness(±2%),
flects the ease of soil detachment by splash or surface flow.
whichwasdeterminedbyrepeatedmeasurementsandslope
TheparameterLS(dimensionless)accountsfortheeffectof length (±12.5m). An error of ±2% was assumed for the
slope length (L) and slope gradient (S) on soil loss. The
grain size analyses as well as for the organic carbon deter-
C factor is the cover factor, which represents the effects of
mination.TheseerrorswerepropagatedthroughtheKfactor
all interrelated cover and management variables (Renard et calculation. An error of ±20% based on the observed vari-
al.,1997).
abilitybetweenspringandautumnofFVContheplotswas
ForcomparabilitybetweentheRUSLEestimatesofKonz
usedforthedeterminationoftheCfactor.FortheRfactoran
et al. (2009) and the ones assessed in this study, we used error of ±5Nh−1, which corresponds to the observed vari-
the same R factor approximation of Rogler and Schwert-
abilitybetweenthesites,wasassumed.Finally,errorpropa-
mann (1981) adapted by Schuepp (1975). According to the
gationforthemultiplicationofthesingleRUSLEfactorswas
USLE procedure, snowmelt can be integrated into erosivity
done.
calculationbymultiplyingsnowprecipitationby1.5andthen
addingtheproducttothekineticenergytimesthemaximum 2.3.3 137Cstoassesstotalnetsoilredistribution
30min intensity. However, the latter proceduredoes not ac-
countforredistributionofsnowbydrifting,sublimationand A2×2inchNaI-scintillationdetector(Sarad,Dresden,Ger-
reducedsedimentconcentrationsinsnowmelt(Renardetal., many)wasusedtomeasuretheinsitu137Csactivity.Thede-
1997).Therefore,assuggestedbyRenardetal.(1997),this tector was mounted perpendicular to the ground at a height
adaption of the R factor was not considered in this study. of25cmtoreducetheradiusoftheinvestigatedareato1m.
The K factor was calculated with the K nomograph after Measurementtimewassetat3600s,andeachsitewasmea-
WischmeierandSmith(1978),usinggrainsizeanalysesand suredthreetimes.
the carbon contents of the upper 15cm of the soil profiles. The detector was successfully (R2=0.86) calibrated
Total C content of soils was measured with a Leco CHN againstgammaspectroscopylaboratorymeasurementswitha
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K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt 3767
20%relativeefficiencyLi-driftedGedetector(GeLi;Prince- contrast, no preferential transport or preferential transport
tonGamma-Tech,Princeton,NJ,USA)attheDepartmentfor of coarse material occurred, most likely due to snow- and
Physics and Astronomy, University of Basel. For the GeLi animal-inducedparticletransport(seeKonzetal.,2012).The
detector, the resulting measurement uncertainty concerning calculation of the erosion rates refers to the period 1986–
the137Cspeakarea(at662keV)waslowerthan8%(errorof 2007because,pre-Chernobyl,137Csisnegligible.Foruncul-
themeasurementat1σ)(Schaubetal.,2010).Gammaspec- tivated sites the diffusion and migration model is an alter-
trometrycalibrationandqualitycontroloftheanalysiswere native to the profile distribution model. However, the 137Cs
performed following the protocol proposed by Shakhashiro depth profile at our reference sites did not follow a polyno-
andMabit(2009). mialdistributionandthusdidnotallowforasuccessfulfitof
Soil moisture influences the measured 137Cs activity. thediffusionandmigrationcoefficient.Duetotheintegrative
Thus, soil moisture measurements with an EC-5 sensor and repeated measurement with the NaI detector, the errors
(DecagonDevices)wereusedtocorrecttheinsitumeasure- associated with measurement precision are assumed to be
ments. The NaI detector has the advantage of providing an largelycancelledout.However,theerrorassociatedwiththe
integrated measurement over an area of 1m2. The com- spatial variability of the reference inventory (±20Bqkg−1)
monlyobserved,intrinsicsmall-scalevariability(∼30%)for waspropagatedthroughtheconversionmodelinordertore-
137Cs (Sutherland, 1996; Kirchner, 2013) is thus smoothed. ceiveanupperandlowerconfidenceintervalfortheresulting
Nonetheless, around 10% of the uncertainty of the 137Cs- erosionestimates.
basedsoilerosionvaluescanbeattributedtothevariabilityof
replicatedmeasurementsoneachsingleplot.Themainerror 2.4 Spatialmodellingofsnowglidedistances
oftheinsitumeasurementresultsfromthepeakareaevalua-
We used the spatial snow glide model (SSGM; Leitinger et
tionandwasdeterminedtobe17%(Schaubetal.,2010).
With the 137Cs method, soil redistribution rates are cal- al., 2008) to predict potential snow glide distances for an
area of approximately 30km2 surrounding our study sites.
culated by comparing the isotope inventory for an eroding
The SSGM is an experimental model, which includes the
pointwithalocalreferenceinventorywhereneithererosion
followingparameters:theforeststand,theslopeangle,win-
nor soil accumulation is expected. In the Ursern Valley, the
initialreference137Csfalloutoriginatedfromthermonuclear ter precipitation, the slope and the static friction coefficient
µ (−). Slope angle and slope aspect were derived from a
weapontestsinthe1950s–1960sandthenuclearpowerplant s
high-precision digital elevation model (DEM) with 2m res-
accidentofChernobylin1986.
Fortheconversionofthe137Csinventoriestosoilerosion olutionandanaccuracyof±0.5mat1σ inopenterrainand
rates, knowledge about the proportion of Chernobyl 137Cs ±1.5mat1σ interrainwithvegetation.Above2000ma.s.l.,
a DEM with 25m resolution and an average error of 1.5m
falloutisakeyparameterfortheestimationoferosionrates;
fortheCentralPlateauandtheJura,2mforthePrealpsand
however,onlyfewdataareavailable.Pre-Chernobyl(1986)
137Cs activities of the top soil layers (0–5cm) of between the Ticino and 3 to 8m for the Alps was used (Swisstopo).
2 and 58Bqkg−1 (one outlier of 188Bqkg−1 in Ticino) Winter precipitation was derived from the MeteoSwiss sta-
tionlocatedinAndermatt.WeusedtheresultfromaQuick-
were recorded for 12 sites distributed across Switzerland
Birdlandcoverclassificationwitharesolutionof2.4m(sub-
(Riesen et al., 1999). After radioactive decay, in 2007, only
1–35Bqkg−1 are left. The 137Cs activity for the flat refer- sequentlyresampledto5m)aslandcoverinput(Meusburger
et al., 2010a). Combining this land cover map with a land
ence sites near the valley bottom (1469–1616ma.s.l) was
estimated as 146±20Bqkg−1 (Schaub et al., 2010). The usemap(MeusburgerandAlewell,2009),itwaspossibleto
derive the parameter forest stand. A uniform static friction
investigated sites are located in close vicinity to the refer-
coefficient(µ )wasassignedtoeachofthefourinvestigated
ence sites and at a comparable altitude (1476–1670ma.s.l). s
landcovertypes.
Consequently, the maximum contribution of pre-Chernobyl
137Csmightrepresent20%atreferencesites. Thestaticfrictioncoefficientcanbederivedby
Additionally, vertical migration must be considered. In F
literature migration values between 0.03 and 1.30cmyr−1 µs = Fr, (6)
n
are reported (Schimmack et al., 1989; Arapis and Karandi-
nos, 2004; Schuller et al., 2004; Schimmack and Schultz, whereFn(gms−2)isthenormalforcethatcanbecalculated
2006;Ajayietal.,2007).IntheUrsernValley,137Csactivity with
(Bqkg−1)declinesexponentiallywithsoildepth.Therefore,
F = m×g ×cosα, (7)
for the conversion of 137Cs measurements to soil erosion n
rates, the well-known profile distribution model (Walling et where g is the standard gravity (9.81ms−2), α is the slope
al., 2011) was adapted for direct use with the 137Cs activ- angle(◦)andmistheweightofthesnowglideshoe(inour
ityprofile(Konzetal.,2009,2012).Wesettheparticlesize study202g).
factorto1becausenopreferentialtransportofthefinersoil The initial force (F ; with the unit gms−2) which is
r
particles was observed for our sites (Konz et al., 2012). In neededtogettheglideshoemovingonthevegetationsurface
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3768 K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt
®
wasmeasuredwithaspringbalance(Pesola Medio1000g)
andmultipliedwiththestandardgravity.Toobtainrepresen-
tativevaluesofF themeasurementwasreplicated10times
r
per sample site and subsequently averaged. The parameter
estimates the surface roughness, induced by different veg-
etation types and land uses. A detailed description of the
model and its parameters has been provided by Leitinger et
al.(2008).
Supplemented by snow glide measurements from this
study, the SSGM (i.e. ordinary least squares, OLS, regres-
sion equation) was refined to be valid also for north-facing
sites and sites with Alnus viridis. Consequently, the revised
SSGMisgivenbytheequation
ln(yˆ)= 0.337−0.925x +0.095x +0.01x
1 2 3
+1.006x +0.839x +0.076x −0.075x2, (8)
4 5 6 7
Figure3.Snowglidedistanceagainstthestaticfrictioncoefficient
where yˆ is the estimated snow-gliding distance (mm), x is
1 forthesouth-andnorth-facingslopesites(representedbysquares
theforeststand(0;1),x istheslopeangle(◦),x isthewin-
2 3 anddots,respectivey).Y errorbarsrepresentthestandarddeviation
terprecipitation(mm),x istheeasternslopeaspect(0;1),x
4 5 ofreplicatemeasurementsatonesite.Forthestaticfrictioncoeffi-
isthesoutherslopeaspect(0;1),x6 isthewesternslopeas- cient,anerrorof±0.1(correspondingtothescaleaccuracyofthe
pect(0;1)andx7isthestaticfrictioncoefficient.Therevised springbalance)wasassumed.
SSGMwashighlysignificant(p<0.001),withadetermina-
tioncoefficientof0.581(adjustedR2).
The model was then applied for the winter period of (sgd=−1547.2µ +172.93;R2=0.50;p=0.036).Forthe
s
2009/2010 (285mm winter precipitation) and for the long-
north-facing slope, the snow glide distances and the vari-
termaveragewinterprecipitation(430mmwinterprecipita-
abilityarelower.Approximately80%oftheobservedvari-
tionfortheyears1959to2010).
ability on the north-facing slope can be explained by the
surface roughness (sgd=−622.17µ +43.09; R2=0.82;
s
p=0.033). The identification of slope aspect and surface
3 Resultsanddiscussion
roughness as main causal factors for snow gliding corre-
3.1 Snowglidemeasurements2009/2010 spondstothefindingsofotherstudies(InderGandandZu-
pancic, 1966; Newesely et al., 2000; Hoeller et al., 2009).
For each site, the static friction coefficient, as a measure of Accordingtoseveralstudiesontheseasonalsnow–soilinter-
surface roughness, was determined in autumn prior to the faceconditions(InderGandandZupancic,1966;McClung
installation of the snow glide shoes. Lowest surface rough- and Clarke, 1987; Leitinger et al., 2008), snow gliding on
ness was observed for the hayfields, followed by soil sur- south-facing sites is preferential in spring, when high solar
face at sites covered with Alnus viridis on the north-facing radiation leads to a high proportion of melting water at the
slope (Table 1). For the pastures without dwarf shrubs, the soil–snow interface. However, in autumn, snow gliding pri-
two mean monitored values differed (µ =0.37 and 0.68) marilyoccurswhenahugeamountofsnowfallsonthewarm
s
but were similar to those of pastures with dwarf shrubs soil. In this case, north-facing sites may be confronted with
(µ =0.66to0.69).Slightlyhighervalueswereobservedfor highsnowglidingactivityaswell.
s
the dense undergrowth of Alnus viridis sites on the south- Our measured snow glide distances are comparable to
facingslope(µ =0.70and0.84).Thesestaticfrictioncoeffi- those recorded by other researchers. For example, during
s
cientsarewithintherangeof0.22–1.18reportedbyLeitinger a 7-year period in the Austrian Alps, Höller et al. (2009)
etal.(2008). monitored a snow glide distance of 10cm within the for-
The snow glide measurements confirmed the presence est,170cminclearedforestsitesandupto320cmforopen
and the potential impact of this process in our investigated fields. Margreth (2007) found total glide distances of 19 to
sites. The mean measured snow glide distances (sgd) of 102cmforan11-yearobservationperiodintheSwissEast-
the different sites varied from 2 to 189cm (see Table 1). ernAlps(south-eastfacingslopeat1540ma.s.l.).
The main part of this variability can be explained by the
slope aspect and the surface roughness (see Fig. 3). With 3.2 Soilerosionestimates
increasing surface roughness (expressed as the static fric-
tion coefficient, µ ) the snow glide distance declines. This Snow glide depositions were observed for seven sites; for
s
decrease is more pronounced for the south-facing slope one site a wet avalanche deposition (pN) and for four sites
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K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt 3769
Table1.Parametersrelatedtomeasuredsnowglidedistance(sgd;SDisthestandarddeviationbasedonthreetofivereplicatemeasurements)
fortheinvestigationsitesintheUrsernValley,Switzerland.Nindicatesthesitesonthenorth-facingslope.
Site Vegetation Slope InitialforceFr Staticfriction Measuredsgd SDsgd
coefficient
(◦) (gms−2) µs(−) (cm) (cm)
h1 hayfield 39 569 0.37 189 117
h2 hayfield 38 510 0.33 50 40
h3 hayfield 35 392 0.24 126 49
pw1 pasturewithdwarfshrubs 38 1030 0.66 34 19
pw2 pasturewithdwarfshrubs 35 1118 0.69 28 15
p1 pasture 38 579 0.37 89 37
p2 pasture 35 1109 0.68 64 40
h1N hayfield 28 343 0.20 30 14
h2N hayfield 30 608 0.35 8 1
pN pasture 18 628 0.33 17 23
A1N Alnusviridis 25 1050 0.58 2 1
A2N Alnusviridis 30 451 0.26 28 9
A1 Alnusviridis 22 1550 0.84 14 18
A2 Alnusviridis 31 1197 0.70 60 46
nosnowglidedepositionswereobserved(Table3).Thefour
siteswithoutsnowglidedepositionswerealllocatedonthe
north-facingslope.Theerosionratesestimatedfromthesed-
imentyieldsofthesnowglidedepositionrangedfrom0.03to
22.9tha−1yr−1.Themaximumvaluewasdeterminedforthe
siteh1whichisinagreementwiththe137Csmethod.Forsites
withsnowglidedepositions,ameanvalueof8.4tha−1yr−1
was measured. The somewhat high erosion rates are doc-
umented in a photo from the spring (Fig. 4). The winter
2012/2013precipitationof407mmwasquiterepresentative
ofthelong-termaverage(i.e.430mm).Onaverage,thepas-
tured sites without dwarf shrubs produced the highest mea-
sured sediment yields, followed by the hayfields, and con-
siderably lower values were observed for the pastures with
dwarfshrubsites.Whethertheobserveddifferenceisdueto
the different vegetation cover or due to site-specific topog-
raphy cannot be solved conclusively with the present data Figure4.Exampleofsnowglidedepositsforthesitep1.
set.AwetavalanchewasobservedforthesitepN.Interest-
ingly, at 1.97tha−1yr−1, the estimated erosion rate of the
tors(duetoahigherfractionalvegetationcover).Thiseffect
wetavalanchedepositionwassmallerthanmostofthesnow-
wasnotcompensatedforbythe,onaverage,higherK factor
gliding-relatederosionrates.However,higherosionratesof
3.7 and 20.8tha−1 per winter due to wet avalanches have of0.40kghN−1m−2 onthenorth-facingslopes.Thehigher
K factor is caused by a 6% higher proportion of very fine
beenreportedatastudysitelocatedintheAostaValley,Italy
sand. The mean RUSLE-based soil erosion rate for all sites
(Ceaglioetal.,2012).Atthisstudysite,wherethemajorsoil
was4.6tha−1yr−1.
lossistriggeredbywetavalanches,thesnow-relatedsoilero-
Themean137Cs-basedsoilerosionrateof17.8tha−1yr−1
sion estimated from the deposition area was comparable to
theyearlytotalerosionratesassessedwiththe137Csmethod is approximately 4 times as high as the average RUSLE es-
(13.4and8.8tha−1yr−1;Ceaglioetal.,2012). timates. Congruent with RUSLE, the 137Cs-based average
soil erosion rate on the north-facing slopes is lower than
Onthenorth-facingslope,anaverageRUSLEestimateof
1.8tha−1yr−1withamaximumvalueof3.8tha−1yr−1was on the south-facing slopes (by 8.7tha−1yr−1). The high-
est137Cs-basedsoilerosionestimatesarefoundattwohay-
established(Table2).The,onaverage,lowervaluesascom-
pared to the south-facing slope (6.7tha−1yr−1) are due to field sites (h1 and h3) and the pasture sites on the south-
facing slope (p1 and p2). The higher RUSLE and 137Cs
lowerslopeangles(thuslowerLSfactorvalues)andC fac-
estimates on the more intensely used, steeper and more
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3770 K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt
Figure5.Snowglideerosionestimatedfromthesnowglidedeposit
sedimentyieldagainstthedifferenceofthe137CsandRUSLEsoil Figure6.Correlationofthecumulativesnowglidedistances(cm)
erosionrate(tha−1yr−1).Y errorbarsrepresenttheuncertaintyof measuredforthewinterof2009/2010versusthedifferenceofthe
both the 137Cs and RUSLE estimates. X error bars represent the 137Cs and RUSLE soil erosion rate (tha−1yr−1) for the grass-
land sites (dots, n=10) and the Alnus viridis sites (A1N, A2N;
standarddeviationoferosionratesresultingfromseveralsediment
squares,n=2).Y errorbarsrepresenttheerrorofboththe137Cs
measurementswithinonesnowglidedeposit.Thesolidlinerepre-
andRUSLEestimates.Xerrorbarsrepresentthestandarddeviation
sents the obtained linear regression and the dotted lines the 95%
ofreplicatesnowglidemeasurementsatonesite.Solidlinerepre-
confidenceinterval.
sents a linear regression and the dotted lines the 95% confidence
interval.
snow-glide-affectedsouth-facingslopearereasonable.How-
ever,thehigh137Cs-basederosionrates(16.6tha−1yr−1for
A1Nand13.7tha−1yr−1 forA2N)atAlnusviridissitesare the sediment yield measurements in the snow glide depo-
unexpectedandwillbediscussedbelow. sition comprise only one winter, a relation (p=0.13) be-
tweenthesnowglideerosionandthedifferenceof137Csand
3.3 Relationbetweensoilredistributionandsnow RUSLE could be observed (R2=0.39; Fig. 5). The largest
gliding difference between 137Cs- and RUSLE-based erosion could
beobservedforsiteswithhighsnow-glide-relatedsediment
Sediment yield measurements in snow glide depositions yield(exceptforthesiteh3).Theresultinginterceptmightbe
showed the importance of this process in the winter of eitherduetoadeviationoftheweatherconditionsinthewin-
2012/2013.However,eventhoughthewinterwasquiterep- terof2012/2013fromthelong-termaverageconditioncap-
resentativeoftheaveragewinterconditions(intermsofwin- turedbytheothermethodsorduetotheimpactofoccasional
ter precipitation), the measured rates are likely to vary be- wetavalanchesand/orsnowmelt.Forinstance,followingthe
tweendifferentyears.Toassesstherelevanceofthisprocess USLEsnowmelt adaptationfor the R factorwould resultin
foralongertimescale,asecondapproachusingRUSLEand an,onaverage,2.1tha−1yr−1 highermodellederosionrate
137Cswasfollowed. forallsites.
Our hypothesis was that the difference of the water soil Afurtherindicationfortheimportanceofsnowglidingas
erosion rate modelled with RUSLE and the total net ero- asoilerosionagentisgivenbythesignificantpositivecorre-
sion measured with the 137Cs method correlates to a “win- lationbetweenmeasuredsnowglidedistanceandthediffer-
tersoilerosionrate”.Thiswintersoilerosionratecomprises enceof137CsandRUSLE,whichweinterpretasthewinter
long-term soil removal by snow gliding and occasionally soilerosionrate(Fig.6).Themeasuredsnowglidedistance
wet avalanches as well as snowmelt. These “winter erosion explained 64% of the variability of the winter soil erosion
rates” (difference of 137Cs and RUSLE) ranged from rates rate (p<0.005). However, this relation does not comprise
of −7.3tha−1yr−1 for a pasture with dwarf shrubs to rates theAlnusviridissitesthatshowedalargedifferencebetween
of 31tha−1yr−1 for the hayfield site h1. According to our RUSLE- and 137Cs-based rates but a short snow glide dis-
hypothesis,anegativedifferenceof137CsandRUSLEindi- tance. For the Alnus viridis sites, we have to expect that ei-
catesasedimentation(becauseRUSLEsimulatesthepoten- theroneofthetwoapproachestodeterminesoilerosionrates
tial water soil erosion rates) and a positive value indicates is erroneous and/or that we have another predominant ero-
erosion due to processes not implemented in the RUSLE. sion process not considered or not correctly parameterised
Themostlikelyprocesseswouldbesnow-inducedprocesses. in the RUSLE yet. A possible error related to the 137Cs ap-
Twoobservationsunderpinourhypothesis:first,eventhough proachmightbethat137Cswasinterceptedbyleafandlitter
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K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt 3771
Table2.Measuredsitecharacteristics(SOCstandsforsoilorganiccarbon;vfsstandsforveryfinesandfraction),resultingRUSLEfactors
andsoilerosionratesand137Cs-basederosionratesfortheinvestigationsitesintheUrsernValley,Switzerland.∗ indicatesthesitesfrom
Konzetal.(2009).
Site Slope SOC vfs Silt Clay Kfactor Pfactor LSfactor Rfactor Cfactor RUSLE 137Cs
(◦) (%) (%) (%) (%) (kghN−1m−2) (−) (−) (Nh−1) (−) (tha−1yr−1) (tha−1yr−1)
h1∗ 39 7.7 12.9 47.3 12.5 0.280 1.00 22.2 97.2 0.010 6.0 37.0
h2∗ 38 7.2 9.7 58.8 17.3 0.290 1.00 8.8 94.5 0.006 1.5 11.0
h3∗ 35 7.4 12.3 43.8 16.9 0.230 1.00 20.7 93.6 0.010 4.5 33.0
pw1∗ 38 6.9 6.3 63.5 10.8 0.320 0.90 12.6 91.7 0.040 13.3 6.0
pw2∗ 35 7.1 11.2 40.9 14.2 0.230 0.90 11.8 94.8 0.040 9.3 13.0
p1∗ 38 7.6 11.2 50.5 11.6 0.270 0.90 11.8 97.6 0.020 5.6 20.0
p2∗ 35 7.2 12.4 45.6 15.0 0.250 0.90 15.3 96.4 0.020 6.6 30.0
h1N 28 4.8 18.5 41.0 5.8 0.416 1.00 7.0 93.6 0.012 3.2 18.3
h2N 30 4.3 13.7 48.0 8.5 0.419 1.00 8.4 91.7 0.012 3.8 7.5
pN 18 6.2 17.5 38.7 10.2 0.369 1.00 1.1 97.2 0.012 0.5 7.2
A1N 25 3.8 16.1 43.8 9.7 0.399 1.00 5.3 93.6 0.003 0.6 16.6
A2N 30 6.8 18.7 39.7 9.6 0.389 1.00 8.4 91.7 0.003 0.9 13.7
Meanofnorth-facingsites 37 7.3 10.9 50.1 14.0 0.267 0.94 14.7 95.1 0.021 6.7 21.4
Meanofsouth-facingsites 26 5.2 16.9 42.2 8.8 0.398 1.00 6.0 93.6 0.008 1.8 12.7
Meanofallsites 32.4 6.4 13.4 46.8 11.8 0.3 1.0 11.1 94.5 0.0 4.6 17.8
Table3.Snow-movement-relatedsoilerosionderivedfromthedifferenceof137Cs-basedandRUSLE-basederosionrates(Diff.)andfrom
fieldmeasuredsedimentinsnowglidedeposits(sgerosion).Foreachsnowglidedeposit,themeansedimentyieldestimateisbasedonseveral
samples(n).SDisthestandarddeviationfortheresultingerosionratesbasedontheindividualsedimentyieldsamplesand∗ indicatesthe
sedimentyieldofawetavalanche.“UncertaintyDiff.”providestheuncertaintyofDiff.resultingfromboththe137CsandRUSLEmethod.
Site RUSLE 137Cs Diff.137Cs−RUSLE UncertaintyDiff. sgerosion SDsgerosion n
(tha−1yr−1) (tha−1yr−1) (tha−1yr−1) (tha−1yr−1) (tha−1yr−1) (tha−1yr−1)
h1 6.0 37.0 31.0 8.5 22.9 81.5 16
h2 1.5 11.0 9.5 7.7 3.2 1.9 3
h3 4.5 33.0 28.5 8.2 1.1 1.9 10
pw1 13.3 6.0 −7.3 10.9 0.8 0.5 3
pw2 9.3 13.0 3.7 9.8 0.0 0.1 7
p1 5.6 20.0 14.4 8.5 16.7 6.8 11
p2 6.6 30.0 23.4 8.6 14.0 44.9 13
h1N 3.2 18.3 15.1 7.6 nosnowglide – –
h2N 3.8 7.5 3.7 8.4 nosnowglide – –
∗
pN 0.5 7.2 6.7 8.0 1.97 3.8 18
A1N 0.6 16.6 16.0 7.2 nosnowglide – –
A2N 0.9 13.7 12.8 7.6 nosnowglide – –
material of Alnus viridis. Thus, a reference site with Alnus tude of the 137Cs-based erosion rate needs to be considered
viridisstockingwouldbenecessarywhichisdifficulttofind carefully. The profile distribution model tends to overesti-
atoursitebecausenoflatareasexistwithAlnusviridisstock- matesoilerosionratessinceitassumesthatthe137Csdepth
ing. The observation of increasing soil erosion with an in- distributiondoesnotchangewithtime.However,inthevery
creasing snow glide rates is congruent with the findings of first years after the fallout, 137Cs was concentrated more in
Leitingeretal.(2008),whoobservedthattheseverityofero- thesurfacesoillayer(SchimmackandSchultz,2006).Thus,
sionattributedtosnowgliding(e.g.torn-outtrees,extensive intheyearsafterthefallout,smalllossesofsoilwouldhave
areasofbaresoilduetosnowabrasion,landslidesintopsoil) resultedinarelativelyhigh137Cslosswhichmightresultin
was high in areas with a high snow glide distance and vice anoverestimationofsoilerosionrates.
versa. The latter uncertainties do not include snowmelt erosion
Generally, for these subalpine sites the magnitude of the andtemporalvariability,bothpotentialreasonsfortheinter-
RUSLE-based water erosion rates needs to be considered ceptobservedbetweenthemagnitudeofwintererosionesti-
with caution not only with respect to the uncertainties in- matedfromRUSLE/137Csandfromsnowglidedepositions.
volvedbutalsoconceptuallysinceseveralofthefactorslay Nonetheless, the almost 1:1 relation is a clear indication
outside the empirical RUSLE framework. Also, the magni- that the observed discrepancies between the RUSLE-and
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3772 K.Meusburgeretal.:Soilerosionbysnowgliding–afirstquantificationattempt
Figure8.Mapofthepotentialsnowglidedistance(m)modelledby
SSGM.
Figure 7. Box plot of measured snow glide distances and corre-
terraces(0.5minwidth)mostlikelyreducesnowglidingbut
spondingmodellingresultsfordifferentlanduse/landcovertypes
arenotcapturedbythedigitalelevationmodelthatisusedfor
(hayfields–h;pastures–p;pastureswithdwarfshrubs–pw;and
abandonedgrasslandcoveredwithAlnusviridis–A)forthewinter theSSGM.Ingeneral,modelledsnowglidedistancesshow
periodof2009/2010.Nindicatesthesitesonthenorth-facingslope. smaller ranges than measured snow glide distances, due to
the5mresolutionofthemodelinputdata(Fig.7).Interest-
ingly,theoccurrenceofdwarfshrubsseemstoreducesnow
137Cs-based soil erosion rates are related to snow gliding. glidingtoalargerextentthanpredictedbythemodel.
Congruentwithourresults,Stanchietal.(2014)foundare- The modelled snow glide distance map (Fig. 8) is based
lation between the intensity of snow-erosion-affected areas on the long-term average of winter precipitation, which, at
andthedifferenceofRUSLEand137Csestimates. 430mm, is clearly higher than the winter precipitation in
Further, it can be deduced that low surface roughness is 2009/2010(at285mm;Fig.7).Thehighestsnowglideval-
correlated to high snow glide distances, and these are, in ues were simulated on the steep, south-facing slopes with
turn, positively correlated to large observed differences be- predominately grassland and dwarf-shrub cover. Very high
tween RUSLE- and 137Cs-based soil erosion rates that we rates are also found on the lower parts of the south-facing
interpretashighwintersoilerosionrates.Erosionestimates slopes that are used as pastures and hayfields. The small-
from sediment yield measurements of the snow glide depo- est snow glide rates are located on the north-facing slopes.
sition could confirm the partially high winter erosion rates. Themapclearlyreproducestheeffectoftopographyandas-
However, the presented relations might be highly variable, pect. Moreover, snow glide distances summarised for pre-
dependingonsoiltemperature(whetherthesoilisfrozenor dominantlandusetypesalsoreproducetheimpactofvegeta-
not)duringsnow,theoccurrenceofawaterfilmthatallows tioncover(Fig.9).Thehighestpotentialsnowglidedistances
atransitionofdrytowetgliding(Haefeli,1948)andonthe were simulated by the SSGM for the south-facing hayfield
weatherconditionsofaspecificwinter.Inaddition,someof and pasture sites, while the Alnus viridis has, on average,
theinvestigatedsitesmightalsobeaffectedbyavalanchesin decidedly smaller snow glide distances. In contrast, on the
otheryears. north-facingslopes,thereisnodifferenceobservedbetween
theAlnusviridisandthehayfieldcategory.Herethepasture
3.4 Modelledsnowglidedistances sitesshowthehighestaveragesnowgliderate.Theinterpre-
tationofthedifferencesbetweenlandusetypesis,however,
The modelled snow glide rates from the SSGM compared restricted since systematically different topographic condi-
reasonablywellwiththesnowglidemeasurements(Fig.7). tionsareinvolved.
Inagreementwiththemeasuredvalues,allsitesfacingtothe Thetopographicandclimaticconditionsinourvalleyre-
northrevealedlowermodelledsnowglidedistances.Largest sembletheenvironmentunderwhichtheSSGMwasinitially
discrepanciesbetweenthemeanmodelledandmeasuredval- developed; nonetheless, further regular yearly measurement
ues of each site occur for the pastures on the south-facing wouldbeneededtoimprovetheperformanceofthemodelin
slopes (p and pw). The model overestimates the snow glide this area. In conclusion, the application of the SSGM high-
ratesforthesesites,whichmightbeduetotheeffectofmi- lightedtherelevanceofthesnowglidingprocessandthepo-
crorelief in form of cattle trails at these sites. These small tentiallyrelatedsoilerosionfor(sub-)alpineareas.
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