Spatial variability of seasonal snow cover at different scales in the Swiss Alps

109 Pages·2011·4.48 MB·English

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Spatial variability of seasonal snow
cover at different scales in the
Swiss Alps
Doctoral Thesis
Author(s):
Egli, Luca Edoardo
Publication date:
2011
Permanent link:
https://doi.org/10.3929/ethz-a-006689712
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In Copyright - Non-Commercial Use Permitted
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Diss. ETH No. 19658
Spatial variability
of seasonal snow cover at different scales
in the Swiss Alps
A dissertation submitted to
ETH ZURICH
for the degree of
Doctor of Sciences
presented by
Luca Edoardo Egli
Dipl. Phys. ETH Zu¨rich
born 28.10.1972
citizen of Ba¨retswil (ZH)
accepted on the recommendation of:
Prof. Dr. Paolo Burlando, examiner
Prof. Dr. John W. Pomeroy, co-examiner
Dr. Tobias Jonas, co-examiner
2011
Summary
Snow is an important reservoir of water in the Swiss Alps. The temporary water storage is
accumulatedbysnowprecipitationduringwintertimeandreleasedasmeltwaterrun-off,which
can contribute to floods in spring. To predict the total volume of snow and the potential water
discharge requires monitoring and modeling of the spatial distribution of alpine snow cover in
basinsofdifferentsizes.
This thesis explores a novel approach to describe and model the statistical properties of snow
depths x during a season at scales from 101 m to 105 m. Beyond the mean snow depth x ,
i i
the standard deviation σ is the simplest quantity to characterize the spatial variability of snow.
This work shows that from an integral watershed perspective these variables are sufficient to
describe the dynamics of snow storage and melt water discharge. A priori all combinations of
x andσ areadmissibleandcanlikewiseoccurinnature. Thisthesishoweverdemonstratesthat
i
themeanandthestandarddeviationofsnowdeptharestrictlyrelatedtooneanotherandcanbe
describedbysimple,butdifferent,relationsduringaccumulationandablation. Asanimportant
practicalimplication,measurementsofthemeansnowdepthallowsnowdepthvariabilitytobe
estimated.
During accumulation and restricted on flat alpine sites, the evolution of σ(x ) closely follows a
i
power law σ(x ) ∼ x β. The parameter β (∼ 0.84) is almost constant for several accumulation
i i
seasons and invariant between scales from 104 m to 105 m. The relation σ(x ) indicates that
i
accuracy in estimating the total volume of snow depends on x , β and the number of snow
i
depthobservationavailableataspecificscale.
To increase the number of snow depth sampling points on flat fields between scales from 104
m to 105 m, additional snow depth data on a regular grid of 7 km over the Swiss Alps can be
derivedfromthenumericalweatherpredictionmodelCOSMO-7. Usingasimpleaccumulation
andablationalgorithm,thesolidprecipitationoutputofCOSMO-7 allowsestimatessnowdepth
alpine-widewithanaccuracysimilartothatofotherautomaticmethodsatthepointscale(∼ 101
m).
During ablation, the relation σ(x ) is very distinct from the accumulation behavior. From this
i
relation, the onset of melting can be determined by snow depth observation alone. The relation
σ(x ) during ablation is surprisingly well reproduced by a model of uniform melt rates. The
i
uniform melt model only requires an initial snow depth distribution, which is formed through
accumulation.
A quantitative analysis on complex topography in a small alpine catchment (scale: ∼ 103 m),
showed that the concept of uniform melt can also be applied to snow depth in sloped terrain.
Initialized with the measured snow distribution and driven by snow depletion rates from a few
snow depth readings from flat sites, the concept of uniform melt allows the total snow volume
and snow covered area during melt to be determined with high accuracy. Further efforts are
neededtodescribetherelationσ(x)oncomplexalpineterraintodeterminetheinitialconditions
fortheuniformmeltmodelintermsofcharacteristicsoftheunderlyingtopography.
Zusammenfassung
In den Schweizer Alpen ist Schnee ein wichtiger Wasserspeicher. Dieses Zwischenreservoir
wird wa¨hrend des Winters durch Schneeniederschla¨ge akkumuliert und als Schmelzwasser im
Fru¨hling wieder frei gegeben, welches zu Hochwassern beitragen kann. Die Abscha¨tzung des
GesamtvolumensderSchneedeckeunddesSchmelzwasserabflusseserfordertdieBeobachtung
undModellierungderra¨umlichenVerteilungdesSchneesinalpinenEinzugsgebietenverschie-
denerGro¨ssen.
DieseDissertationuntersuchteinenneuenAnsatz,umdiestatistischenEigenschaftenderSchnee-
ho¨hen x wa¨hrend ihres saisonalen Verlaufs auf ra¨umlichen Skalen zwischen 101 m und 105 m
i
zu beschreiben und zu modellieren. Ausser des Mittelwertes x ist die Standardabweichung
i
σ der Schneeho¨hen die einfachste Gro¨sse um die Verteilung des Schnees zu charakterisieren.
Diese Arbeit zeigt, dass fu¨r die integrale Betrachtung eines gesamten Einzugsgebietes, diese
beiden Variabeln genu¨gen, um die Dynamik der Schneespeicherung und des Schmelzwasser-
abfluss zu beschreiben. A priori sind alle Kombinationen von x und σ annehmbar und ko¨nnen
i
gleichermassen in der Natur auftreten. Diese Dissertation zeigt aber, dass der Mittelwert und
die Standardabweichung der Schneeho¨hen grundsa¨tzlich zueinander in Beziehung stehen und
dieser Zusammenhang mit einfachen aber verschiedenen Regeln wa¨hrend der Akkumulations-
undSchmelzphasebeschriebenwerdenkann.AlseinewichtigeImplikationdieserVerknu¨pfung
gehthervor,dassderMittelwertderSchneeho¨hendieVariabilita¨tderSchneedeckebestimmt.
Wa¨hrendderAkkumulationsphaseundeingeschra¨nktaufalpineFlachfelder,folgtdieEntwick-
lung von σ(x ) anna¨hernd einem Exponentialgesetz σ(x ) ∼ x β. Der Parameter β (∼ 0.84) ist
i i i
fu¨rverschiedeneAkkumulationsjahrefastgleichunddieserWertwirdauchaufra¨umlichenSka-
len zwischen 104 m bis 105 m beibehalten. Die Funktion σ(x ) zeigt, dass die Genauigkeit mit
i
welcherdasGesamtvolumenderSchneedeckeabgescha¨tztwerdenkann,vonx ,β undderAn-
i
zahl von Schneeho¨henmessungen abha¨ngt, welche auf einer spezifischen Skala zur Verfu¨gung
stehen.
Um die Anzahl der Schneeho¨henstichproben auf Flachfeldern innerhalb der Skalen zwischen
104 m und 105 m zu erho¨hen, ko¨nnen zusa¨tzliche Schneeho¨henwerte auf einem regelma¨ssigen
Rastervon7kmu¨berdenSchweizerAlpenvomnumerischenWettervorhersagemodell(COSMO-
7) abgeleitet werden. Mittels eines einfachen Akkumulations und Schmelzalgorithmus werden
die Schneeniederschlagsdaten von COSMO-7 verwendet, um Schneeho¨hen in den Alpen mit
einera¨hnlichenGenauigkeitabzuscha¨tzen,wieesverschiedeneautomatischeMethodenaufder
Punktskala(∼ 101 m)vermo¨gen.
Wa¨hrend der Schmelzsphase ist die Beziehung σ(x ) sehr unterschiedlich von derjenigen der
i
Akkumulationsphase.DurchdasunterschiedlicheVerhaltenla¨sstsichderBeginnderSchmelz-
phase nur mittels Schneeho¨henmessungen bestimmen. Der Verlauf von σ(x ) wa¨hrend der
i
Schmelzphasela¨sstsicherstaunlichgutmiteinemModellvonra¨umlichkonstanten,uniformen
Schmelzraten reproduzieren. Dieses Modell beno¨tigt als Anfangsbedingung nur die Schnee-
ho¨henverteilung,welchewa¨hrendderAkkumulationsphasegebildetwurde.
Zusammenfassung iii
Eine quantitative Analyse der Entwicklung der Schneeverteilung auf unregelma¨ssiger Topo-
graphie eines kleinen Einzuggebietes (Skala: ∼ 103 m) zeigt, dass das Konzept der unifor-
men Schmelzraten auch fu¨r Schneeho¨hen auf unebenen Untergrund angewendet werden kann.
DaskonzeptionelleModellwirddurchSchmelzratenvoneinpaarwenigenSchneeho¨henpunkt-
messungen auf Flachfeldern angetrieben und mit einer gemessenen Schneeho¨henverteilung in-
itialisiert. Durch dieses Vorgehen kann das Gesamtvolumen der Schneedecke und der Schnee-
bedeckungsgrad wa¨hrend der Abschmelzphase mit einer grossen Genauigkeit bestimmt wer-
den. Ku¨nftige wissenschaftliche Anstrengungen sind no¨tig, um die Funktion σ(x ) auf unre-
i
gelma¨ssigeralpinerTopographiezubeschreiben,welchedieAnfangsbedingungfu¨rdasunifor-
meSchmelzmodellinAbha¨ngigkeitderzugrundeliegendenTopographiebestimmt.
Contents
Summary i
1 Introduction 1
1.1 Theimportanceofwaterstoredinthemountainsnowcover . . . . . . . . . . . 1
1.2 Thespatialdistributionofsnowandpreviousworks . . . . . . . . . . . . . . . 2
1.2.1 MonitoringthespatialdistributionofsnowintheSwissAlps . . . . . . 2
1.2.2 Physicalprocessesformingthespatialdistributionofsnow . . . . . . . 3
1.2.3 Differentmodelapproaches . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Workinghypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Outlineofthethesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Comparisonofdifferentautomaticmethodsforestimatingsnowwaterequivalent 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 DataandMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 Manualreferencemeasurements . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 AutomaticMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.3 Datapreparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.4 ParametersofstatisticalComparison . . . . . . . . . . . . . . . . . . . 19
2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.1 SnowWaterEquivalent(SWE) . . . . . . . . . . . . . . . . . . . . . . 22
2.3.2 DailyNewSnowWaterEquivalent(HNW) . . . . . . . . . . . . . . . 23
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.1 AutomaticMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.2 SWEestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4.3 HNWestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3 HystereticdynamicsofseasonalsnowdepthdistributionintheSwissAlps 31
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 DataandMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
vi Contents
3.3.1 Accumulationperiod . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.2 Ablationperiod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.1 Accumulationperiod . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.2 Ablationperiod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.3 RelationofAccumulationandAblation . . . . . . . . . . . . . . . . . 37
3.5 Extendingthehysteresistoabroaderrangeofterrainconditions . . . . . . . . 37
4 Avirtualnetworkforestimatingdailynewsnowwaterequivalentandsnowdepth
intheSwissAlps 39
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2.1 COSMO-7ModelOutputData . . . . . . . . . . . . . . . . . . . . . . 40
4.2.2 PointMeasurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3.1 VirtualNetwork(VN) . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3.2 ParametersofVerification . . . . . . . . . . . . . . . . . . . . . . . . 44
4.4 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.4.1 HNW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.4.2 HS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.3 SpatialAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.5 ConclusionandOutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5 Seasonal development of spatial snow depth variability across different scales in
theSwissAlps 53
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2.1 Automaticandmanualpointmeasurements . . . . . . . . . . . . . . . 55
5.2.2 COSMO-7modeloutputdata . . . . . . . . . . . . . . . . . . . . . . 55
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.3.1 Parameterβ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.3.2 Scaleanalysisandsamplingprocedure . . . . . . . . . . . . . . . . . . 56
5.4 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.4.1 Scaleanalysisof β . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.4.2 Accuracyofestimatingthemeanofsnowcover . . . . . . . . . . . . . 61
5.5 ConclusionsandOutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
6 DynamicsofsnowablationinasmallAlpinecatchment 65
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
6.2 DataandMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.2.1 Testsite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.2.2 TerrestrialLaserScannerTechnology . . . . . . . . . . . . . . . . . . 67
6.2.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.2.4 MethodsandParametersofComparison . . . . . . . . . . . . . . . . . 70
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.3.1 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.3.2 Model-uniformmeltingfromdifferentinitialdistributions . . . . . . . 73
6.3.3 Model-differentmeltschemesfromobservedinitialdistribution . . . . 74
6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.4.1 TLSmeasurementdrivenuniformmeltmodel . . . . . . . . . . . . . . 77
6.4.2 Distributiondrivenuniformmelt . . . . . . . . . . . . . . . . . . . . . 78
6.4.3 Differentmeltschemesfromobservedinitialdistribution . . . . . . . . 79
6.4.4 Implicationsforsnow-hydrologicalmodeling . . . . . . . . . . . . . . 80
6.5 ConclusionandOutlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7 Synthesis,ConclusionandOutlook 83
7.1 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7.3 OutlookforfurtherResearch . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.3.1 Modeling snow depth distribution depending on the underlying topog-
raphy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.3.2 Extendingtheconceptofthe”VirtualNetwork” . . . . . . . . . . . . . 91
7.3.3 Precipitationradar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
7.3.4 Opticalairbornescanner . . . . . . . . . . . . . . . . . . . . . . . . . 92
7.3.5 Snowpatternsinthemountains . . . . . . . . . . . . . . . . . . . . . 93
References 96
Acknowledgements 103
Chapter 1
Introduction
1.1 The importance of water stored in the mountain snow cover
Snow is a reservoir of fresh water in mountainous regions and constitutes a component of the
seasonal mountain water cycle. For many countries in the world, the resource of fresh water
from snow melt plays an important role in local drinking water supplies (Meisser and Reller,
2005; Woddwell, 2004) and agricultural irrigation (Pimentel et al., 2004). Vegetation growth
and diversity in mountainous ecosystems are strongly linked to the available water content of
the snow cover and its spatial distribution on the ground (Walker et al., 1993). In snow clima-
tology, spatio-temporal changes in the snow cover and the onset of melting are investigated to
predict the future availability of water resources for both fresh-water supplies and hydropower
production (Barnet et al., 2005). These aspects of snow water storage and melt water run-off
are of particular interest in the field of snow hydrology (DeWalle and Rango, 2008) and their
scientificinvestigationoftheseisofpracticalimportanceforsocietyingeneral.
For the Swiss society and economy, the management of snow water resources is essential for
hydropower generation and for preventing natural hazards. In contrast to some other regions
in the world, a significant percentage of precipitation above Swiss sub-alpine regions falls as
snow and a temporary water storage is accumulated, mainly during winter, as snow. Snow wa-
terequivalent(SWE)isreleasedasmeltwaterduringsnowablation, whichoccursprimarilyin
springorearlysummerinalpineregions.
Quantifying the total volume of water stored in the form of snow in the Swiss mountains is es-
sentialtoestimatetheavailablepotentialenergyforlocalhydropowercompanies. Furthermore,
predicting the timing of the melt water release is economically crucial to optimize the pricing
ofthisformofenergyproductionintheinternationalelectricitymarket.
Melt water run-off originating from snow may also contribute to severe natural hazards in the
Swiss mountains and the valleys downstream. Warm temperatures in early summer, combined
with heavy liquid precipitation, can rapidly melt snow, and the subsequent run-off can increase
the severity of floods, erosion and sediment transport. Such a situation occurred in the year
1999 after a catastrophic avalanche season during which heavy snow loads accumulated in the
Alpine region (SLF, 2000). During the melting season, the extraordinary amount of snow in
conjunction with rain storms led to flooding in early summer 1999, causing significant damage
and financial loss. The event highlighted the relevance of monitoring snow water resources in
theSwissAlpsforbetternationalfloodprediction.
The Swiss Federal Office for the Environment (the national authority for flood prevention and
forecasting)recentlystartedincludingsnowhydrologicalinformationinitsperiodichydrologi-
cal bulletin (http://www.hydrodaten.admin.ch/lhg/ hydroinfd.pdf). The main reason for includ-
2 CHAPTER 1
ing of snow observations is to capture the total amount of snow and the subsequent snow melt
dischargeforwatershedsatdifferentscales. Forthisthespatialdistributionofsnowisacrucial
variable.
Motivated by the general importance of snow water resources worldwide and the requirements
of the Swiss authorities, this thesis attempts to reflect the availability of different monitoring
techniques and based on this to investigate a novel scientific approach for the description and
modeling of the distribution of snow at scales between 101 m and 105 m. The study focus is
on the Swiss Alps, where compared to many other mountainous regions in the world a denser
network of automatic and manual snow measurements is available. The large Swiss network
wasthereforestudiedtocontributetoabetterunderstandingofspatialdistributiondynamicsof
snow, not only at the scale of the whole of Switzerland, but also at the scale of a small alpine
catchment. Thescientificresultsandmodelapproachesofthisworkmaylikelytobeapplicable
in other mountainous regions with sparse observational data but with similar meteorological
conditionstothoseintheSwissAlps.
In the following, the current status of various scientific efforts to capture the spatial variability
of snow is reviewed in order to formulate working hypotheses and specific research questions
forthisdissertation.
1.2 The spatial distribution of snow and previous works
Understandingspatialsnowcovervariabilityiscrucialforproperlyestimatingthetotalvolume
of snow or SWE for basins of different sizes. If a homogeneous snow cover can be assumed, a
single point measurement will clearly be sufficient to capture the total volume of snow. Snow
cover in mountainous regions, however, is highly variable in space and time (Balk and Elder,
2000; Blo¨schl, 1999; Deems et al., 2006). This complicates estimating total snow volumes
especially in regions where few measurements are available. The greater the variability of
snow depth, the more point measurements will be needed to capture the mean snow amount.
Moreover, from a hydrological perspective, the spatial variability of snow at the onset of melt
determinesthetimingofthetotalrun-off(SeyfriedandWilcox,1995).
1.2.1 MonitoringthespatialdistributionofsnowintheSwissAlps
During the last decades substantial efforts have been made to monitor the extent of snow cover
and daily snowfall in the Swiss Alps. A network of automatic snow measurement stations
(Rhyner et al., 2002) and manual observers (Marty, 2008) have been set up to assess the na-
tional, regional and local avalanche risk situation. This risk is evaluated daily and published
during winter time in the Swiss national avalanche danger bulletin. The existing network con-
tains more than 150 point measurements spread over the Swiss Alpine region. This network
allows daily access to observational snow data and effective monitoring of the temporal snow
accumulationandablationpatterns.
Integrating this network into snow hydrological applications presents, however, several princi-
pal challenges: i) snow depth readings are typically provided instead of SWE, ii) point mea-
surements do not capture the spatial variability of snow cover for a large heterogeneous area
around the stations, and iii) snow depth readings are restricted to flat sites. The work of this