Impact of snow gliding on soil redistribution

27 Pages·2013·7.96 MB·English

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Impact of snowSoglid lEiadrthipen Acceng on soil SolidDi sEcuassriotnhspen Acce iscuss K.Meusburgeretal.
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redistribution for a sub-alpine area in
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Switzerland The Cryospherepen Acc The CryoDsispchusesiornespen Acc per Abstract Introduction
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Conclusions References
K. Meusburger1, G. Leitinger2, L. Mabit1, M. H. Mueller1, and C. Alewell1 D
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1EnvironmentalGeosciences,UniversityofBasel,Basel,Switzerland us
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2InstituteofEcology,UniversityofInnsbruck,Innsbruck,Austria io J I
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Received:15May2013–Accepted:17June2013–Published:18July2013 ap J I
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Correspondenceto:K.Meusburger([email protected]) r
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PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
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Abstract D
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Theaimofthisstudyistoassesstheimportanceofsnowglidingassoilerosionagent s
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for four different land use/land cover types in a sub-alpine area in Switzerland. The 14 io 10,9505–9531,2013
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investigated sites are located close to the valley bottom at approximately 1500ma.s.l., P
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5 while theelevation of the surroundingmountain ranges is about2500ma.s.l. Weused pe Impact of snow
two different approaches to estimate soil erosion rates: the fallout radionuclide 137Cs r gliding on soil
andthe RevisedUniversalSoilLoss Equation(RUSLE).TheRUSLE modelissuitable | redistribution
to estimate soil loss by water erosion, while the 137Cs method integrates soil loss due D
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to all erosion agents involved. Thus, we hypothesise that the soil erosion rates deter- c K.Meusburgeretal.
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10 mined with the Cs method are h13ig7her and that the observed discrepancy between sio
theerosionrateofRUSLEandthe Csmethodisrelatedtosnowgliding.Cumulative n
snow glide distance was measured for the sites in the winter 2009/2010 and modelled Pa TitlePage
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for the surrounding area with the Spatial Snow Glide Model (SSGM). Measured snow e
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glide distance range from 0 to 189cm with lower values for the north exposed slopes.
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We observed a reduction of snow glide distance with increasing surface roughness of Conclusions References
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thevegetation,whichisanimportantinformationwithrespecttoconservationplanning is
and expected land use changes in the Alps. Our hypothesis was confirmed, the differ- cu Tables Figures
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ence of RUSLE and 137Cs erosion rates was correlated to the measured snow glide s
distance (R2=0.73; p<0.005). A high difference (lower proportion of water erosion ion J I
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20 compared to total net erosion) was observed for high snow glide rates and vice versa. ap J I
The SSGM reproduced the relative difference of the measured snow glide values be- er
tween different land use/land cover types. The resulting map highlights the relevance Back Close
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ofsnowglidingforlargepartsoftheinvestigatedarea.Basedontheseresults,wecon-
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clude that snow gliding is a key process impacting soil erosion pattern and magnitude is
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in sub-alpine areas with similar topographic and climatic conditions. u
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1 Introduction D
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While rainfall is a well-known agent of soil erosion, the erosive forces of snow move- s
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mentsarehardlyknown.Particularlywetavalanchescanyieldenormouserosiveforces io 10,9505–9531,2013
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thatareresponsibleformajorsoilloss(Gardner,1983;Ackroyd,1987;Belletal.,1990; P
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5 Jomelli and Bertran, 2001; Heckmann et al., 2005; Fuchs and Keiler, 2008; Freppaz pe Impact of snow
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et al., 2010) also in the avalanche release area (Ceaglio et al., 2012). gliding on soil
Besides avalanches another important process of snow movement affecting the soil | redistribution
surfaceissnowgliding(InderGandandZupancic,1966).Snowglidingistheslow(mm D
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to cm per day) downhill motion of a snowpack over the ground surface caused by the c K.Meusburgeretal.
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10 stress of its own weight (McGraw-Hill and Parker, 2002). Snow gliding predominantly◦ sio
occurs on south-east to south-west facing slopes with slope angles between 30–40 n
(In der Gand and Zupancic, 1966; Leitinger et al., 2008). Two main factors that control Pa TitlePage
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snow glide rates are (i) the wetness of the boundary layer between the snow and soil e
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cover and (ii) the ground surface roughness determined by the vegetation cover and
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rocks (McClung and Clarke, 1987; Newesely et al., 2000). So far, only few studies Conclusions References
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investigated the effect of snow gliding on soil erosion (Newesely et al., 2000; Leitinger Dis
et al., 2008). A major reason for this shortcoming is the difficulty to obtain soil erosion cu Tables Figures
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rates caused by snow processes. In steep sub-alpine areas soil erosion records (e.g. s
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with sediment traps) are restricted to the vegetation period because avalanches and n
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20 snow gliding can irreversibly damage the experimental design (Konz et al., 2012). ap J I
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Recentlyfirstphysicallybasedattemptstomodeltheerosiveforceofwetavalanches r
weredone(Confortolaetal.,2012).Nosimilarmodelexistsforsnowgliding.However, Back Close
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the potential maximum snow glide distance during a targeted period can be modelled
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with the empirical spatial snow glide model (SSGM) (Leitinger et al., 2008). The mod- is
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elling of this process is important to evaluate the impact of the snow glide process on u
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soil erosion at larger scale. io
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Soil erosion rates can be obtained by direct quantification of sediment transport in P InteractiveDiscussion
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the field, by fallout radionuclides (FRN) based methods (e.g. Mabit et al., 1999; Ben- p
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mansour et al., 2013; Meusburger et al., 2013) and by soil erosion models (Nearing D
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et al., 1989; Merritt et al., 2003). Since the end of the 1970’s empirical soil erosion c HESSD
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modelssuchastheUniversalSoilLossEquation(USLE;WischmeierandSmith,1965; s
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WischmeierandSmith,1978),anditsrefinedversionstheRevisedUSLE(RUSLE;Re- n
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5 nardetal.,1997)andtheModifiedUSLE(MUSLE;Smithetal.,1984),havebeenused a
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worldwide to evaluate soil erosion magnitude under various conditions (Kinnell, 2010). e Impact of snow
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These well-known models allow the assessment of sheet erosion and rill/inter-rill ero- gliding on soil
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sion under moderate topography. However, they do not integrate erosion processes redistribution
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associated with wind, mass movement, tillage, channel or gully erosion (Risse et al.,
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10 1993;Mabitetal.,2002;Kinnell,2005)andalsosnowimpactduetomovementormelt- u
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ing is not considered (Konz et al., 2009). Several models have been tested for steep s
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alpinesiteswiththeresultthatRUSLEisreproducedthemagnitudeofsoilerosion,the n
relativepatternandtheeffectofthevegetationcovermostplausible(Konzetal.,2010; Pa TitlePage
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Meusburgeretal.,2010b).TheerosionratederivedfromRUSLEcorrespondstowater r Abstract Introduction
erosion induced by rainfall and surface runoff and hence in our site to the soil erosion
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processes during the summer season without significant influence of snow processes. Conclusions References
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In contrast, the translocation of FRN reflects all erosion processes by water, wind is
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and snow during summer and winter season and thus is an integrated estimate of the u
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total net soil redistribution rate since the 1950s (the start of the global fallout deposit). io J I
137 134 n
Anthropogenicfalloutradionuclides(e.g. Cs, Cs)havebeenusedworldwidesince
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decades to assess the magnitude of soil erosion and sedimentation processes (Mabit p J I
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and Bernard, 2007; Mabit et al., 2008; Matisoff and Whiting, 2011). The most well- r
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known conservative and validated anthropogenic radioisotope used to investigate soil |
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redistribution and degradation is Cs (Mabit et al., 2013).
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For (sub-)alpine areas the different soil erosion processes captured by RUSLE and is
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the 137Cs method result in different erosion rates (Konz et al., 2009; Juretzko, 2010; us
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Alewell et al., 2013). However, this difference might also be due to several other rea- io
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sons such as the error of both approaches, the non-suitability of the RUSLE model for P InteractiveDiscussion
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this specific environment and/or the erroneous estimation of the initial fallout of 137Cs. p
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In this study, we aim to investigate, whether the observed discrepancy between ero- D
sionratesestimatedwithRUSLEandtheonesprovidedbythe137Csmethodcanbeat isc HESSD
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least partly attributed to snow gliding processes. Since vegetation cover affects snow ss
gliding, four different sub-alpine land use/land cover types were investigated. The sec- ion 10,9505–9531,2013
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5 ond objective of our research is to assess the relevance of snow gliding processes at a
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catchment scale using the Spatial Snow Glide Model (SSGM). e Impact of snow
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gliding on soil
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2 Materials and methods D
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2.1 Site description s
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The study sites are located in Central Switzerland (Canton Uri) in the Ursern Valley
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10 (Fig.1).TheelevationoftheW–Eextendedvalleyrangesfrom1400upto2500ma.s.l. p
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Themeanannualrainfall,averagedbetween1986and2008,is1516mm.Themeanair r Abstract Introduction
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temperature measured at an altitude of about 1480m a.s.l is 3.1 C (MeteoSchweiz). |
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The valley is snow covered from November to April with a mean annual snowfall of
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443mm in the period 1986 to 2008. Drainage of the basin is usually controlled by isc Tables Figures
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15 snowmelt from May to June. Important contribution to the flow regime takes place dur- ss
ingearlyautumnfloods.Thelanduseischaracterisedbyhayfieldsnearthevalleybot- io J I
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tom(from1400toapproximately1600ma.s.l.)andpasturingfurtherupslope.Siliceous P
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slopedebrisandmorainematerialisdominantatoursites,andformsCambicPodzols p J I
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(Anthric) and Podzols (Anthric) classified after IUSS Working Group (2006).
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Of the 14 experimental sites, 9 are located at the south-facing slope and 5 at the |
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north-facing slope at altitudes between 1450 and 1600ma.s.l. Four different land D FullScreen/Esc
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use/cover types with 3–5 replicates each were investigated: hayfields (h), pastures c
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(p), pastures with dwarf shrubs (pw), and abandoned grassland covered with Alnus ss Printer-friendlyVersion
viridis(A).VegetationofhayfieldsisdominatedbyTrifoliumpratense,Festuca,Thymus ion
serpyllum and Agrostis capillaries. For the pastured grassland Glubelaria cordifolia, P InteractiveDiscussion
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Festuca sp. and T. serpyllum dominate. Pastures with dwarf shrubs are dominated by e
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Callunavullgaris,Vacciniummyrtillus,Festucaviolacea,AgrostiscapillariesandT.ser- D
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pyllum. At pasture sites of the south exposed slope, which are stocked from June to c HESSD
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September, cattle trails traverse to the main slope direction. s
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2.2 Snow glide measurement P
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We measured cumulative snow glide distances with snow glide shoes for the winter r
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2009/2010. The snow glide shoe equipment was similar to the set-up used by In der |
redistribution
GandandZupancic(1966),Neweselyetal.(2000)andLeitingeretal.(2008).However, D
no data logger was used to capture the snow glide rates for specific time intervals isc K.Meusburgeretal.
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during the winter. The set-up consisted of a glide shoe and a buried weather-proof s
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box with a wire drum. Displacement of the glide shoe causes the drum to unroll the io
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wire. The total unrolled distance was measured in spring after snowmelt. To prevent P TitlePage
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entanglement with the vegetation, the steel wire was protected by a flexible plastic e
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tube.
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Conclusions References
2.3 Assessment of soil redistribution pattern based on RUSLE and the 137Cs D
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For 7 sites, RUSLE and 137Cs based erosion rates were available from Konz io J I
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etal.(2009)andforthe6additionalsitesweappliedthesamemethodsforsoilerosion P
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assessment with 137Cs and RUSLE than in Konz et al. (2009). The 137Cs measure- p J I
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ments were decay corrected for comparison purpose.
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2.3.1 137Cs to assess total net soil redistribution
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A2inch×2inchNaI-scintillationdetector(Sarad,Dresden,Germany)wasusedtomea- u
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sure the in-situ Cs activity. The detector was mounted perpendicular to the ground io
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ataheightof25cmtoreducetheradiusoftheinvestigatedareato1m.Measurement P InteractiveDiscussion
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time was set at 3600s and each site was measured three times. p
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Thedetectorwascalibratedagainstgammaspectroscopylaboratorymeasurements D
with a 20% relative efficiency Li-drifted Ge detector (GeLi; Princeton Gamma-Tech, isc HESSD
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Princeton,NJ,USA)attheDepartmentforPhysicsandAstronomy,UniversityofBasel. ss
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The resulting measurement uncertainty on Cs peak area (at 662 keV) was lower n
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5 than8%(errorofthemeasurementat1-sigma).Gammaspectrometrycalibrationand a
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quality control were performed following the protocol proposed by Shakhashiro and e Impact of snow
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Mabit (2009). A detailed description of the calibration procedure of the in-situ detector gliding on soil
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is provided by Schaub et al. (2010). For the conversion of the Cs inventories to soil redistribution
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erosion rates we used the model as described by Konz et al. (2009).
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10 2.3.2 Assessment of soil redistribution by water erosion using the RUSLE sio
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The RUSLE (Wischmeier and Smith, 1978) is an empirical model originally developed P TitlePage
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in the United States. Several adapted versions for other regions as well as for differ- pe
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ent temporal resolutions have been developed and applied with more or less success
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(Kinnell, 2010). Despite its well-known limitation (highlighted in our introduction), we
Conclusions References
selected RUSLE because of the lack of simple soil erosion models specific for moun- D
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tain areas and moreover because of its better performance when compared to the c Tables Figures
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other existing models (Konz et al., 2010; Meusburger et al., 2010b). The RUSLE can s
be calculated using the following equation: ion J I
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A=R·K ·LS·C·P (1) pe J I
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where A is the predicted average annual soil loss (tha−1yr−1). R is the rainfall- runoff- |
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erosivity factor (Nh−1) that quantifies the effect of raindrop impact and reflects the rate D FullScreen/Esc
of runoff likely to be associated with the rain (Wischmeier and Smith, 1978). The soil isc
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erodibilityfactorK (Nhkgm−2)reflectstheeaseofsoildetachmentbysplashorsurface ss Printer-friendlyVersion
flow. The parameter LS (dimensionless) accounts for the effect of slope length (L) and ion
slope gradient (S) on soil loss. The C-factor is the cover factor, which represents the P InteractiveDiscussion
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effects of all interrelated cover and management variables (Renard et al., 1997). pe
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For comparability between the RUSLE estimates of Konz et al. (2009) and the ones D
assess in this study we used the same R factor of 97Nh−1. The K factor was cal- isc HESSD
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culated with the K nomograph after Wischmeier and Smith (1978) using grain-size ss
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analyses and carbon contents of the upper 15cm of the soil profiles. Total C content n
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5 of soils was measured with a Leco CHN analyzer 1000, and grain size-analyses were a
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performed with sieves for grain sizes between 32 and 1000µm and with a Sedigraph e Impact of snow
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5100 (Micromeritics) for grain sizes between 1 and 32µm. L and S were calculated gliding on soil
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after Renard et al. (1997). The support and practice factor P (dimensionless) was set redistribution
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to 0.9 for some of the pasture sites because alpine pastures with cattle trails resemble
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10 small terrace structures, which are suggested to be considered in P (Foster and High- cu K.Meusburgeretal.
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fill, 1983). For all other sites, P value was set to 1. The cover-and-management factor s
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C was assessed for sites with and without dwarf shrubs separately using measured n
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fractional vegetation cover (FVC) in the field. a
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Forinvestigatedsiteswithoutdwarfshrubs(USDepartmentofAgriculture,1977)the r Abstract Introduction
C factor can be estimated with:
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Conclusions References
C=0.45·e−0.0456·FVC (2) D
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for sites with dwarf shrubs the following equation was used: s
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C=0.45·e−0.0324·FVC (3) P
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The FVC was determined in April and September using a grid of 1m2 with a mesh r
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width of 0.1m . The visual estimate of each mesh was averaged for the entire square |
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meter. This procedure was repeated four times for each plot. The maximum standard D FullScreen/Esc
deviation was approx. 5%. is
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2.4 Spatial modelling of snow glide distances io
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Weusedthespatialsnowglidemodel(SSGM,Leitingeretal.,2008)topredictpotential ap
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snow glide distances for an area of approximately 30km surrounding our study sites. r
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The SSGM is an experimental model, which includes 6 main parameters: the forest D
stand (0; 1), the slope angle (◦), the winter precipitation (mm), the slope exposition E isc HESSD
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(0; 1), the static friction coefficient µ (–) and the slope exposition W (0; 1). Slope and ss
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aspect were derived from the digital elevation models DHM25 and below 2000ma.s.l. n
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5 the DOM (Swisstopo). The DOM is a high precision digital surface model with 2m a
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resolution and an accuracy of ±0.5m at 1σ in open terrain and ±1.5m at 1σ in terrain e Impact of snow
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withvegetation.TheDHM25hasaresolutionof25mwithanaverageerrorof1.5mfor gliding on soil
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the Central Plateau and the Jura, 2m for the Pre-Alps and the Ticino and 3 to 8m for redistribution
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the Alps (Swisstopo). Winter precipitation was derived from the MeteoSchweiz station
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10 locatedinAndermatt.WeusedtheresultfromaQuickBirdlandcoverclassificationwith u
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aresolutionof2.4m(subsequentlyresampledto5m)aslandcoverinput(Meusburger s
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et al., 2010a). Combining this land cover map with a land use map (Meusburger and n
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Alewell, 2009), it was possible to derive the parameter forest stand. To each of the 4 a
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investigated land cover types a uniform static friction coefficient (µs) was assigned. er Abstract Introduction
The static friction coefficient can be derived by:
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Conclusions References
F D
µs= Fr (4) iscu Tables Figures
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where F is the normal force that can be calculated with ion J I
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Fn=m·g·cosα (5) pe J I
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wheregthegravitationalconstant(9.81ms−1),αistheslopeangle(◦)andmtheweight |
of the snow glide shoe (in our study 202g). D FullScreen/Esc
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The initial force (F ), which is needed to get the glide shoe moving on the vege- c
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tation surface, was measured with a spring balance (Pesola Medio 1000g). To ob- s Printer-friendlyVersion
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tain representative values of F the measurement was replicated 10 times per sam- n
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ple site and subsequently averaged. The parameter estimates the surface roughness, a
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which integrates the effect of different vegetation types and land uses on snow gliding. e
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A detailed description of the model and its parameters has been provided by Leitinger D
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et al. (2008). c HESSD
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Themodelwascalibratedwiththemeasuredsnowglidedistancesand285mmwin- s
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terprecipitation(sumoftheprecipitationfromDecember2009toApril2010).Afterthe n
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5 calibration, potential snow glide distances with long-term average winter precipitation a
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of 443mm (years 1959 to 2010) were modelled. e Impact of snow
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3 Results and Discussion D
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3.1 Snow glide measurements 2009/2010 s
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For each site the static friction coefficient as measure for surface roughness was de- n
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10 termined in autumn prior to the installation of the snow glide shoes. Lowest surface p
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roughness was observed for the hayfields, followed by sites covered with Alnus viridis r Abstract Introduction
on the north exposed slope, where only little undergrowth was observed (Table 1). |
For the pastures with dwarf-shrubs, the two mean monitored values differed (0.04 and Conclusions References
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0.07) but were similar to that of pastures without dwarf-shrubs (0.06 to 0.07). Slightly isc Tables Figures
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15 higher values were observed for the dense undergrowth of Alnus viridis sites on the ss
south exposed slope (0.07–0.08). io J I
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Themeasuredsnowglidedistancesofthedifferentsitesvariedfrom1to189cm(see P
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Table 1). A main proportion of this variability can be explained by the slope exposition p J I
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andthesurfaceroughness(seeFig.2).Withincreasingsurfaceroughness(expressed
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as the static friction coefficient; µ ) the snow glide distance declines. This decrease |
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is more pronounced for the south exposed slope (y =−1547.2µ +172.93; R2=0.50; D FullScreen/Esc
p=0.036). For the north exposed slope the snow glide distancses and the variability isc
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are lower. Approximately 80% of the observed variability on the north exposed slope s Printer-friendlyVersion
can be explained by the surface roughness (y =−622.17µ +43.09; R2=0.82; p= ion
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25 0.033). For the south exposed slope, the snow glide distances are higher and only a
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50% of the variability can be explained by the difference in surface roughness. The e
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