The influence of the snow cover on the temperature regime of steep rock walls
THESIS
Presented to the Faculty of Science of the University of Fribourg (Switzerland) in consideration for the award of the academic grade ofDoctor rerum naturalium
by
Anna Haberkorn from
Mühlhausen, Germany
Thesis No: 1971 UNIprint
2016
This work would not have been possible without the support of many people whom I would like to thank first:
My direct supervisors Marcia Phillips and Martin Hoelzle always supported me, not only technically but also by encouraging me. It was a pleasure to work with them, to get insights in my field of studies from their different perspectives and to profit from their explicit knowledge on mountain permafrost. I would like to thank both for their guidance through- out the last four years. They supported me in whatever direction my work developed and helped to bring it to a successful finish.
Furthermore I would like to thank our project collaborators Michael Krautblatter and Daniel Draebing within the DACH project “Influences of snow cover on thermal and me- chanic processes in steep permafrost rock walls” for their engagement in joint research work and advice. This dissertation was carried out in the context of the DACH Lead Agency and funded by the Swiss National Science Foundation SNF (project no. 200021E-135531).
Carlo Danioth and his colleagues at Gemsstock (SkiArena Andermatt-Sedrun) are thanked for their valuable support with field logistics. Special thanks go to Hansueli Rhyner who was responsible for safety in the field and always gave a helping hand during field work, as well as Robert Kenner who was responsible for terrestrial laser scanning. The SLF electronics-, mechanics and IT team is thanked for their excellent work and help whenever I needed them. Useful input with the used models was provided by Michael Lehning, Nander Wever and Walter Steinkogler, who I especially would like to thank.
I thank my colleagues from the permafrost and snow climatology group for the unique working atmosphere: Marcia, Christoph, Robert, Edgar and Rachel. Moreover I would like to thank my colleagues of the Geography Unit at the University of Fribourg. I appreciated the enjoyable working atmosphere and help with any questions when I visited. Thanks to all other colleagues and friends for the extraordinary four years at the SLF in Davos and for all the memorable moments we shared.
Finally I want to thank my mother who always believes in me and who supports every decision in my life, as well as Thiemo for always supporting and encouraging me when needed.
Davos, 08 June 2016 Anna Haberkorn
Observations show that considerable amounts of snow can accumulate in steep, rough rock walls. The heterogeneously distributed snow cover significantly affects the surface energy balance and hence the thermal regime and stability of frozen rock walls. In rock wall thermal modelling a part of the energy balance of steep bedrock is thus only poorly described or missing when assuming a lack of snow in terrain exceeding 50◦.
To assess the spatial and temporal small-scale variability of snow depth in rugged rock faces and its influence on rock temperatures and rock stability, a multi-method approach is applied at the Gemsstock– (central Swiss Alps, 2961 m a.s.l.) and Steintaelli ridges (Valais Swiss Alps, 3150 m a.s.l.), both located at the lower fringe of permafrost. While the snow cover is observed using remote snow depth distribution data derived from winter terrestrial laser scans and time-lapse photographs, the thermal impact of the snow on the rock is investigated combining continuous near-surface rock temperature measurements (0.1 m depth) distributed over the north and south facing rock walls, seismic refraction tomogra- phy and borehole rock temperature measurements (Gemsstock). Additionally, crackmeters are used to measure rock kinematics at Steintaelli.
The snow depth distribution is strongly determined by the rock wall micro-topography.
Around 2 m of snow can accumulate on slopes with slope angles up to 75◦due to micro- topographic structures like ledges. However, a thermal insulation of the ground starts already with snow depths exceeding 0.2 m in the steep, bare rock faces. At the rock wall- scale the accumulation of a thick, long lasting snow cover in most parts of the rock faces smoothes contrasts in mean annual rock surface temperature between north and south facing slopes. Differences are less than 4◦C. However, within the rock walls small-scale heat fluxes between adjacent snow-covered and snow-free locations are pronounced and caused by the strongly varying micro-topography and micro-climate in the rock walls. In addition to thermal effects close to the surface, the initial timing, depth and duration of the seasonal snow cover control the active layer thickness and mechanic properties of the rock walls. The insulating effect of the snow cover on rock temperatures favours ice segregation, while pronounced daily fluctuations of rock temperatures during snow-free periods cause short-term fracture kinematics. Their repetitive occurrence destabilizes the rock walls. In contrast, the mechanical impact of snowmelt water infiltration into open fractures on rock wall stability at depth was found to be negligible at the investigated sites. This is due to the development of a thick, continuous and impermeable basal ice layer which forms between the snow cover–rock interface during snowmelt.
In parallel to observations, the effects of the snow cover on the surface energy balance and on the thermal regime of the rock walls were modelled both at selected points in- and distributed over the rugged, steep north and south facing rock walls. The models used are the 1 dimensional energy balance model SNOWPACK and the distributed, process based model Alpine3D. The latter in combination with a precipitation scaling method based on terrestrial laser scans to introduce varying snow depth distribution. The performance of the models and their uncertainties are discussed and evaluated against a dense network of near-surface rock temperature measurements and high resolution (0.2 m) remote snow cover observations.
Snow cover – rock interactions are convincingly modelled in the heterogeneous rock walls.
The challenge of integrating representative winter precipitation input and its redistribution by wind, as well as avalanches is accounted for using the precipitation scaling approach and greatly improves results. An increase of mean annual near-surface rock temperature due to the accumulation of snow in both sun-exposed and shaded rock walls was measured and confirmed by model results.
The results of this thesis show the strong controlling effect of the snow cover on the rock thermal regime and on mechanical properties. This knowledge is essential for mod- elling heterogeneous snow depth distribution and rock temperatures in highly variable terrain. The applicability of distributed, physics based models simulating the snow cover in steep bedrock has been shown, as well as an attempt to introduce varying snow depths in steep rock. This rock wall-scale approach now needs to be implemented in permafrost distribution models covering larger areas.
Beobachtungen zeigen, dass beträchtliche Mengen Schnee in steilen, schroffen Felswän- den akkumulieren können. Die unregelmässige räumliche Verteilung der Schneedecke beeinflusst stark die Energiebilanz der Felsoberfläche und somit auch das vorherrschende Temperaturregime der gefrorenen Felswände und deren Stabilität. Vernachlässigt man den Schnee in Felstemperaturmodellierungen in Felswänden steiler als 50◦, so ist ein Teil der Energiebilanz unzureichend beschrieben.
Um die kleinräumige Verteilung und zeitliche Variation der Schneedecke in steilen, schroffen Felswänden zu evaluieren und somit deren Einfluss auf Felstemperaturen und Felsstabilität, werden verschiedenste Methoden angewandt. Diese verteilt über die Nord- und Süd exponierten Felswände des Gemsstock- (Zentralschweizer Alpen, 2961 m ü.d.M.) und des Steintaelli Grates (Walliser Alpen, 3150 m ü.d.M.), welche beide am unteren Hö- henniveau des Permafrostvorkommens in den Alpen angesiedelt sind. Schneedecken Beob- achtungen werden aus der Ferne mittels terrestrischem Laserscanning und automatischen Kamerabildern durchgeführt und geben Aufschluss über die stark variierende Schneehö- henverteilung in den Felswänden. Um den thermische Einfluss der Schneedecke auf den Fels abschätzen zu können, werden verschiedene Messungen kombiniert: kontinuierliche Felstemperaturmessungen in 0.1 m Tiefe, verteilt über die Nord- und Süd exponierten Felswände, messen dominierende Einflüsse nahe der Oberfläche, während Felstemperatur- messungen in einem Bohrloch (am Gemsstock), sowie refraktionsseismischen Messungen, Aufschluss über die Temperaturverteilung innerhalb der Felswänden geben. Die Felskine- matik wiederum, wird mit Crackmetern gemessen.
Die kleinräumig stark variierende Topographie und Struktur der Felswände bestimmt die Schneehöhenverteilung. Aufgrund von Felsstufen in den Wänden, kann bis zu 2 m Schnee in 75◦steilen Bereichen akkumulieren. Die thermische Isolierungswirkung des Schnees auf den Boden fängt jedoch schon ab 0.2 m Schneehöhe in steilem Fels an. Eine, an den meisten Orten der steilen Felswände, mächtige, sowie lange andauernde Schneedecke, glättet die mittleren jährlichen Felsoberflächentemperaturunterschiede zwischen Nord- und Süd exponierten Felswänden. Die gemessenen Temperaturunterschiede sind daher kleiner als 4◦C. Allerdings treten in den Felswänden oft starke Wärmeflüsse auf kleinstem Raum auf, diese meist zwischen benachbarten schneebedeckten und schneefreien Fel- sen. Die Felsstabilität, sowie die Dicke der Auftauschicht werden zusätzlich noch durch das Einschneidatum, die maximale Schneehöhe und die Schneedauer in den Felswänden kontrolliert. Der isolierende Effekt der Schneedecke auf Felstemperaturen begünstigt die
Eis-Segregation, während in der schneefreien Zeit grosse tägliche Felstemperaturschwan- kungen Kluftbewegungen verursachen, was zur Destabilisierung von Felswänden führen kann. Im Gegensatz dazu hat die Infiltration von Schneeschmelzwasser in offene Klüfte keinen mechanischen Einfluss auf die Felsstabilität in den untersuchten Gebieten. Dies wird der Bildung einer dicken und undurchlässigen basalen Eisschicht zugeschrieben, wel- che sich während der Schneeschmelze zwischen der Schneedecke und der Felsoberfläche bildet.
Zusätzlich zu den beschriebenen Messungen, wird die Auswirkung der Schneedecke auf die Oberflächenenergiebilanz steiler Felswände und somit auf Felstemperaturen, model- liert. Da Punktmodellierungen allein nicht ausreichen, werden Felstemperatursimulationen zusätzlich auf die schroffen, steilen Nord- und Süd exponierten Felswände ausgeweitet.
Dazu werden das 1 dimensionale SNOWPACK- und das 3 dimensionale Alpine3D Modell verwendet. Um die unregelmässig verteilten Schneehöhen in den Felswänden simulieren zu können, wird das Alpine3D Modell mit einer Niederschlagsskalierung, basierend auf gemessenen Schneehöhen, kombiniert. Modellresultate und Modellunsicherheiten werden mittels zahlreicher Felsoberflächentemperaturmessungen und der hochaufgelösten (0.2 m) Schneehöhendaten validiert.
Die Interaktion zwischen der Schneedecke und der Felsoberfläche in dem steilen, hete- rogenen Gelände, wird gut durch die Modelle repräsentiert. Durch die Niederschlagsskalie- rung, welche die Schneeumverteilung durch Wind und Lawinen indirekt berücksichtigt, wird die stark variierende Schneehöhenverteilung in den Felswänden erfolgreich simuliert.
Modellergebnisse zeigen, dass durch die Akkumulation von Schnee in den Felswänden die mittlere jährlichen Felsoberflächentemperatur in sonnigen, sowohl als auch in schattigen Felswänden ansteigt, was Messungen bestätigen.
Die Resultate dieser Doktorarbeit verdeutlichen den grossen Einfluss des Schnees auf Felstemperaturen und mechanische Eigenschaften schroffer, steiler Felswände. Dieses Wis- sen trägt wesentlich dazu bei, dass die stark variierende Schneehöhenverteilung und deren Auswirkung auf Felstemperaturen, modelliert werden kann. Es konnte gezeigt werden, dass 3 dimensionale, Prozess basierende Modelle nützlich sind, um die Schneedecke in steilem Fels zu simulieren. Eine Niederschlagsskalierung ermöglicht dabei die Berücksichtigung kleinräumiger Schneehöhenvariation in den Felswänden. Dieser Ansatz ist momentan nur für kleine Gebiete anwendbar (Felswand-Skala) und sollte deshalb, in weiterer Folge, in großskalige Permafrost Modelle implementiert werden.
Abstract iii
Zusammenfassung v
List of Figures xi
List of Tables xiii
List of Abbreviations xv
List of Symbols xvii
1 Introduction 1
1.1 Mountain permafrost . . . 1
1.2 Influence of the snow cover on permafrost ground . . . 2
1.3 Steep rock wall permafrost . . . 4
1.4 The thermal regime of steep rock walls . . . 5
1.5 Snow accumulation in steep rock walls . . . 7
1.6 Modelling permafrost distribution . . . 9
1.7 Research goals and open research questions . . . 11
1.8 Outline of this thesis . . . 12
2 Snow drives steep rock wall temperatures 15 2.1 Introduction . . . 16
2.2 Study site . . . 18
2.3 Methods . . . 19
2.3.1 Near-surface rock temperature measurements . . . 19
2.3.2 Snow cover detection on the basis of NSRT . . . 23
2.3.3 Terrestrial laser scanning . . . 23
2.3.4 Snow pits . . . 24
2.3.5 Multiple linear regression analysis . . . 24
2.4 Results and discussion . . . 25
2.4.1 Snow cover and surface roughness . . . 25
2.4.2 Snow depth at temperature loggers . . . 28
2.4.3 Local variability of NSRT in the rock wall . . . 29
2.4.4 Multiple linear regression analysis . . . 31
2.4.5 Variability of NSRT at selected loggers . . . 34
2.5 Conclusions and outlook . . . 38
3 A 1d modelling approach 41 3.1 Introduction . . . 42
3.2 Study area and measurement design . . . 43
3.3 Methods . . . 44
3.3.1 Rock surface and rock temperature measurements . . . 44
3.3.2 Meteorological data and parameterization . . . 47
3.3.3 Snow cover observations . . . 52
3.4 SNOWPACK modelling . . . 54
3.5 Results and discussion . . . 55
3.5.1 Rock surface temperature . . . 55
3.5.2 Rock temperature measurements . . . 61
3.5.3 Snow depth . . . 63
3.6 Conclusions . . . 67
4 A 3d modelling approach 71 4.1 Introduction . . . 72
4.2 Study site . . . 74
4.3 Methods . . . 74
4.3.1 Near-surface rock temperature . . . 74
4.3.2 Terrestrial laser scanning . . . 76
4.3.3 Distributed energy balance modelling . . . 77
4.4 Results . . . 80
4.4.1 Spatial snow cover variability . . . 80
4.4.2 NSRT variability at selected points . . . 81
4.4.3 MANSRT variability in the rock walls . . . 84
4.4.4 Modelled surface energy balance at selected points . . . 89
4.5 Discussion . . . 93
4.5.1 Model uncertainties . . . 93
4.5.2 Impacts of snow in rock walls . . . 94
4.5.3 Influences of grid resolution . . . 95
4.6 Conclusions . . . 96
4.7 Outlook . . . 97
5 Thermal and mechanical response of rock walls 99 5.1 Introduction . . . 100
5.2 Research area . . . 101
5.3 Methods . . . 102
5.3.1 Meteorological data . . . 103
5.3.2 Snow cover observations . . . 103
5.3.3 Near-surface rock temperature . . . 104
5.3.4 Rock temperature modelling . . . 104
5.3.5 Seismic refraction tomography . . . 105
5.3.6 Crackmeter measurements . . . 106
5.4 Results . . . 106
5.4.1 Meteorological analysis . . . 106
5.4.2 Snow cover distribution . . . 109
5.4.3 Near-surface rock temperature . . . 109
5.4.4 Modelled rock temperature . . . 111
5.4.5 Seismic refraction tomography . . . 113
5.4.6 Crackmeter measurements . . . 114
5.5 Discussion . . . 117
5.5.1 Snow cover distribution . . . 117
5.5.2 Thermal impact of snow on rock temperatures . . . 120
5.5.3 Combination of thermal investigation techniques . . . 121
5.5.4 Mechanical response and implications for rock stability . . . 122
5.6 Conclusions . . . 124
6 Discussion 127 6.1 Observations of the thermal regime of steep rock walls and their mechanical response . . . 127
6.2 Modelling . . . 130
6.3 Resolution issues . . . 132
7 Conclusions 135 8 Outlook 137 References 141 Appendices 161 A1 Intermittent water flow in a rock ridge 163 A1.1 Introduction . . . 164
A1.2 Site description . . . 166
A1.3 Methods . . . 167
A1.3.1 Determination of fracture characteristics . . . 167
A1.3.2 Seismic refraction tomography . . . 169
A1.3.3 Borehole temperature measurements . . . 170
A1.3.4 Near-surface rock temperature measurements . . . 171
A1.3.5 Terrestrial laser scanning . . . 171
A1.3.6 Snow pits . . . 171
A1.3.7 Automatic time-lapse photography . . . 172
A1.3.8 Meteorological data . . . 172
A1.4 Results and discussion . . . 172
A1.4.1 Geometry of rock joints at the surface and inferred at depth . . . 172
A1.4.2 Borehole temperatures . . . 175
A1.4.3 Thermal anomalies in the borehole . . . 175
A1.4.4 Positive and negative thermal anomalies . . . 177
A1.4.5 Snowmelt . . . 179
A1.4.6 Vapour transport in joints . . . 180
A1.4.7 Basal ice layer . . . 181
A1.4.8 Mechanical implications of water infiltration and air circulation in open fractures . . . 183
A1.5 Conclusions . . . 183
List of publications 187
List of talks and selected conferences 189
Curriculum Vitae 191
1.1 Heterogeneous snow cover distribution . . . 3
1.2 Snowmelt patterns . . . 3
1.3 Rock falls . . . 5
1.4 Near-vertical and variably inclined rock walls . . . 7
1.5 Snow accumulation in rock walls . . . 8
2.1 Overview of the Gemsstock study area . . . 20
2.2 Photographs of the N and S facing study slopes . . . 21
2.3 Snow depth evolution . . . 22
2.4 Snow depth as a function of slope angle . . . 26
2.5 Roughness and snow depth distribution in the rock walls . . . 27
2.6 Surface offset variability in the rock walls . . . 30
2.7 Cumulative sum of NSRT . . . 32
2.8 NSRT at selected locations . . . 36
2.9 Relation between NSRT and snow depth. . . 37
3.1 Overview of the Gemsstock study area . . . 45
3.2 Photographs of the N and S facing rock walls . . . 46
3.3 Temporal coverage of the data-set . . . 48
3.4 Cross-section of the Gemsstock ridge . . . 57
3.5 Measured and modelled temperature data . . . 58
3.6 Modelled temperature data . . . 59
3.7 Snow depth evolution . . . 62
3.8 Measured and modelled rock thermal regime . . . 64
3.9 Snow depth distribution over the N and the S facing rock walls . . . 66
4.1 Overview of the Gemsstock study area and the Alpine3D modelling domain 75 4.2 Snow depth distibution . . . 82
4.3 Measured and modelled NSRT at selected locations . . . 85
4.4 Relation between measured and modelled NSRT . . . 86
4.5 Boxplot of measured and modelled MANSRT variability in the rock walls . . 88
4.6 Modelled MANSRT variability for different snow conditions . . . 90
4.7 Modelled energy balance . . . 92
5.1 Overview of the Steintaelli study area . . . 102
5.2 Methods applied . . . 103
5.3 Crackmeter setup . . . 107
5.4 Snow depth evolution and distribution . . . 108
5.5 Time-lapse photographs of snow depth distribution . . . 110
5.6 Measured NSRT at selected locations . . . 112
5.7 Modelled rock thermal regime at selected locations . . . 113
5.8 SRT measurement results . . . 115
5.9 Time-lapse SRT . . . 116
5.10 Crackmeter measurements . . . 118
5.11 Close-up of crack-top temperatures and expansion . . . 119
5.12 Compariosn of measured and modelled ground freezing depths . . . 122
A1.1 Aerial photograph of the Gemsstock study site . . . 167
A1.2 Photographs of the N and the S facing rock walls . . . 168
A1.3 Outline of Gemsstock ridge . . . 169
A1.4 Pole plot of fracture orientation . . . 173
A1.5 SRT of the Gemsstock ridge . . . 174
A1.6 Measured ground thermal regime . . . 176
A1.7 Atmospheric and rock temperature measurements . . . 178
A1.8 Detailed view of borehole temperatures . . . 179
A1.9 Cavity hoar in joint apertures . . . 181
A1.10 Basal ice layer at the snow–rock interface . . . 182
2.1 Data overview . . . 19
2.2 Temperature analysis of the rock walls . . . 29
2.3 Temperature analysis at individual locations in the rock walls . . . 33
2.4 Multiple linear regression models . . . 33
3.1 SWR error calculation . . . 51
3.2 Rock thermal properties . . . 55
3.3 Topographic characteristics at logger locations . . . 56
3.4 Seasonal temperature variation at logger locations . . . 61
3.5 Snow cover duration . . . 61
4.1 Topographic characteristics of selected locations . . . 76
4.2 Rock temperature analysis at selected logger locations . . . 84
4.3 Measured and modelled temperature analysis of the rock walls . . . 89
5.1 Period overview and corresponding air temperatures . . . 107
5.2 Topographic characteristics and NSRT analysis at selected locations . . . 111
AL Active layer
ALT Active layer thickness AWS Automatic weather station
BH Borehole
BRT Borehole rock temperature CT Crack-top temperature DEM Digital elevation model DLB Distance to ledge below GPS Global positioning system
HS Snow depth (measured perpendicular to snow surface)
iB iButton
IMIS Intercantonal Measurement and Information System in Switzerland LWR Longwave radiation
MAA Mean annual amplitude MAAT Mean annual air temperature MAE Mean absolut error
MAGST Mean annual ground surface temperature MANSRT Mean annual near-surface rock temperature MBE Mean bias error
MHS Mean annual snow depth MSAT Mean summer air temperature MWAT Mean winter air temperature
N North
NE North–east
NSRT Near-surface rock temperature
NW North–west
p Level of significance
PISR Potential incoming solar radiation
R Ridge
RMSE Root mean square error
RT Rock temperature
r2 Coefficient of determination
S South
SCD Snow covered period
SE South–east
SLF WSL Institute for Snow and Avalanche Research SLF SO Surface offset
SRT Seismic refraction tomography STD Standard deviation
SW South–west
SWE Snow water equivalent SWR Shortwave radiation
TA Air temperature
TOA Terrestrial solar radiation at the top of atmosphere TLS Terrestrial laser scanning
1d One-dimensional
2d Two-dimensional
3d Three-dimensional
c Fraction of cloud cover
k Clearness index
Lc s Clear sky incoming longwave radiation W m−2
L↓ Incoming longwave radiation W m−2
L↑ Outgoing longwave radiation W m−2
o f f setr eg Snow–climate region
Qcl Quotient of the cloud transmission coefficients
Qg r ound Ground heat flux W m−2
Ql at ent Latent heat flux W m−2
Qnet Net radiation flux W m−2
Qr ai n Flux of sensible and latent heat by liquid precipitation W m−2
Qsensi bl e Sensible heat flux W m−2
Qmel t Melt heat flux W m−2
Sc s Clear sky global radiation W m−2
Smeasur ed Measured global radiation W m−2
Ssc al ed Scaled global radiation W m−2
S↓ Incoming shortwave radiation W m−2
S↑ Outgoing shortwave radiation W m−2
S0 Solar constant W m−2
T A Air temperature ◦C
w Precipitable water cm
Z Solar zenith angle ◦
²a Atmospheric emissivity
²c s Clear sky emissivity
ρbmod Snow bulk density kg m−3
σ Stefan-Boltzmann constant W m−2K−4
τas Atmospheric transmission coefficient for aerosol attenuation τcl Atmospheric transmission coefficient for cloud transmission τg Atmospheric transmission coefficient for gas absorption τr Atmospheric transmission coefficient for Rayleigh-scattering τw Atmospheric transmission coefficient for water vapour
1.1 Mountain permafrost
Permafrost is a climate driven thermal phenomenon, which is widespread in high latitude as well as high alpine mountain regions. Permafrost is defined as ground remaining at temperatures below 0◦C for at least two consecutive years (e.g. van Everdingen 1998).
Permafrost may contain ice, which affects the hydrological and geotechnical properties of the substrate. Changes of the ground thermal state due to climate change (IPCC 2013) are causing shifts in the ice and water content of the ground with influences on slope stability (Hoelzle et al. 2001). In the densely populated Alps recent ground warming trends measured in boreholes (Harris et al. 2009; PERMOS 2013) affect infrastructure, are leading to increases in rock fall activity and debris flow occurrence and have even contributed towards large scale natural disasters (Fischer et al. 2010; Haeberli et al. 1997; Huggel et al. 2005) due to deep-seated instabilities (Gruber et al. 2004b; Ravanel and Deline 2011; Ravanel et al. 2013).
In the European Alps the permafrost distribution is strongly variable over short distances due to the complex mountain topography. The more complex the local topography, the big- ger the spatial heterogeneity of climate-related influencing factors, like insolation and local terrain shading effects (Mott et al. 2011), cloudiness, wind and precipitation distribution (Grünewald et al. 2014), which all affect ground temperatures. Site specific topographic and climatic factors influence the interaction of energy exchange processes between the atmosphere and the earth surface at different spatial and temporal scales (Mittaz et al.
2000). In combination with the ground thermal properties these factors determine the ground temperature regime. Changes of local conditions at the ground surface e.g. due to air circulation through coarse rock debris (Gruber and Hoelzle 2008; Hanson and Hoelzle 2004; Harris and Pedersen 1998) or in fractures of steep bedrock (Hasler et al. 2011b) modify the importance of influencing factors on the ground energy balance (Hoelzle et al. 2001).
Additionally snowmelt water percolating into fractures possibly influences ground stability (Gruber and Haeberli 2007; Hasler et al. 2011a; Blikra and Christiansen 2014). However, Hoelzle et al. (2001) found that both the net radiation and the snow cover are the key factors driving ground temperatures in mountainous terrain. The seasonal snow cover is therefore the main regulator of the ground thermal regime.
1.2 Influence of the snow cover on permafrost ground
In Alpine environments, snow depth and its distribution are spatially and temporally highly variable, shown in Fig. 1.1 (Seligman 1936; Grünewald et al. 2010; Pomeroy and Gray 1995).
This is due to complex topography interacting with local meteorological factors. The local micro-topography drives prevailing wind fields and thus snow erosion and redistribution (Lehning et al. 2008; Mott and Lehning 2010; Grünewald et al. 2014). In addition spatially varying ablation processes caused by local shading of solar radiation from surrounding terrain (Mott et al. 2011) therefore increase the spatial and temporal heterogeneity of the seasonal snow cover (Fig. 1.1). The heterogeneously distributed snow cover is a key driving factor influencing the ground thermal regime (Smith 1975; Goodrich 1982) and strongly controls the presence or absence of permafrost in the Alps (e.g. Keller and Gubler 1993).
This is due to the high surface albedo and longwave emissivity of the snow cover (Keller and Gubler 1993), the low thermal conductivity of the snowpack depending on snow density and micro-structure (Fierz and Lehning 2001), as well as an increased energy consumption during snowmelt (Zhang 2005). The cooling or warming effect of snow on the ground thermal regime depends on snow depth (Luetschg et al. 2008; Phillips and Schweizer 2007), initial timing and depth of the snowpack and snow cover duration (Hoelzle et al. 2003;
Zhang 2005). The early onset of a sufficiently thick snow cover exceeding at least 0.6 to 0.8 m (Hanson and Hoelzle 2004; Keller and Gubler 1993; Luetschg et al. 2008), effectively decouples the ground from cold atmospheric conditions in gently inclined, blocky terrain due to increasing thermal resistance with increasing snow depth (Keller and Gubler 1993).
This results in warmer MAGSTs here (Coutard and Francou 1989; Keller and Gubler 1993;
Luetschg et al. 2008; Matsuoka and Sakai 1999). In contrast, an initially thin snow cover (< 0.15 m) causes pronounced ground cooling due to low thermal resistance of a thin snowpack in combination with the intensification of longwave emissivity and albedo at the snow surface (Keller and Gubler 1993; Luetschg et al. 2008). A long lasting snow cover protects the ground surface from high air temperatures (Zhang 2005) and strong radiation input (Bernhard et al. 1998) during the months with the most intense radiation. The heterogeneous snow distribution found in alpine terrain thus induces a strongly variable local micro-climate, which controls the ground thermal conditions and causes a fine-scale variability of MAGST (Gisnås et al. 2014; Gubler et al. 2011). The variability of MAGST increases steadily from homogeneous (smooth) to heterogeneous (rough) terrain surfaces and with increasing slope angle. Gubler et al. (2011) found MAGST variations of up to 2.5◦C in 10×10 m grids in heterogeneous terrain. The spatial differentiation of MAGST in complex terrain, however, follows repeated patterns (Fig. 1.2), which are driven by recurring annual patterns of snow depth distribution (Gisnås et al. 2014; Mott and Lehning 2010).
Only the magnitude of MAGST is subject to inter-annual variations, depending especially on seasonal snow conditions (Isaksen et al. 2002; Hoelzle et al. 2003; Gruber et al. 2004b;
Etzelmüller et al. 2007; Hipp et al. 2012).
Figure 1.1:Highly variable snow cover at Wannengrat, Eastern Swiss Alps in autumn (Photograph:
C. Groot-Zwaaftnik).
Figure 1.2:Recurring snowmelt patterns on a permafrost scree slope (Muot da Barba Peider, Eastern Swiss Alps). (a) 24 June 2012, (b) 16 July 2013 (Photographs: SLF time-lapse camera).
1.3 Steep rock wall permafrost
Steep bedrock permafrost is widespread in the Alps and has aroused interest in recent years due to natural hazards linked to permafrost degradation in steep rock (Fischer 2010; Huggel et al. 2005). Rock fall events (Fig. 1.3) can be a risk to people and mountain infrastructure (Bommer et al. 2010). They appear to be linked to warming-induced thaw of ice-bonded frozen discontinuities in rock walls (Gruber and Haeberli 2007). Although the ground ice delays the response to climate change due to the latent heat required to attain complete ice melt, steep rock faces react quickly to ongoing climate change (Gruber et al. 2004a) compared to debris covered talus slopes or rock glaciers. This is due to the largely absence of an insulating block layer, snow cover or vegetation in steep rock faces (Hoelzle et al.
2001; Mittaz et al. 2000) and thus, the direct coupling of surface and subsurface conditions to atmospheric processes (Gruber et al. 2004a). This hypothesis is however possibly only valid for near-vertical, snow-free, compact rock. The situation is different in rock faces with complex micro-topography. The large spatial heterogeneity of rugged rock walls with their complex geological structure cause a complex interaction with the local climate. Rock temperatures are thus strongly influenced by modulating factors, such as local terrain shading effects, the accumulation of a snow cover or water flow in fractures (Moore et al.
2011). In rugged, fractured rock walls the interactions of the atmosphere, the ground and rock mechanic conditions are still poorly understood (e.g. Krautblatter et al. 2012;
Krautblatter and Moore 2014). However, changing bedrock temperatures and if ground ice is present, ice temperatures alter the prevalent mechanical properties of frozen rock walls with effects on their stability (Gruber et al. 2004b; Fischer et al. 2010; Kenner et al.
2011; Pirulli 2009; Davies et al. 2001; Krautblatter et al. 2013). Ice segregation and ice wedging widen joints and contribute to rock weathering (Matsuoka 2001; Murton et al.
2006). Increasing rock temperatures cause a loss of shear strength of ice-bonded joints and reduce the stability of frozen bedrock (Davies et al. 2001), which may lead to rock wall failure (Fischer and Huggel 2008; Krautblatter et al. 2013). The relation between numerous large rock slides in the European Alps to climate change and the loss of rock wall stability during the last decades has been shown in various studies (e.g. Huggel et al. 2008; Ravanel and Deline 2011; Ravanel et al. 2010, 2013).
The thermal response of permafrost to atmospheric warming and thus the frequency and magnitude of rock slope destabilization takes place at different time scales and depths (Gruber et al. 2004b). As an almost immediate reaction (delay of months or years) to an increase in surface temperatures, the active layer thickens, resulting in rapid destabilization (Gruber and Haeberli 2007). Pronounced rock fall activity originating in permafrost areas were observed in the hot summer 2003 (Gruber et al. 2004b; Schiermeier 2003) or more recently during the summer 2015 heat wave (SLF permafrost rock fall database) in the European Alps and can be attributed to increasing seasonal thaw depths (Fig. 1.3 a). In contrast large, deep-seated rock slope instabilities (Fig. 1.3 b) are delayed by decades,
centuries or even millennia. They may be related to an increase in permafrost temperatures, which cause a rise of the lower permafrost boundary and change rock and ice mechanical properties at depth (Fischer et al. 2010; Krautblatter et al. 2013; Ravanel and Deline 2011).
Investigating the rock thermal regime close to the surface (e.g. Gruber et al. 2003, 2004a,b;
Gruber and Haeberli 2007; Hasler et al. 2011b; Hipp et al. 2014; Magnin et al. 2015a) and at depth (Hasler et al. 2011b; Magnin et al. 2015a; Noetzli et al. 2010; PERMOS 2013; Wegmann 1998), as well as the mechanical regime (e.g. Hasler et al. 2011a, 2012; Krautblatter et al.
2013) of steep rock walls is therefore crucial.
Figure 1.3:(a) Rock fall (5000 m3) triggered in the active layer during the summer 2015 heat wave at Piz Cambrena, Eastern Swiss Alps (Photograph: D. Hunziger). (b) Deep-seated slope fail- ure (150000 m3) released in February 2014 at Piz Kesch, Eastern Swiss Alps (Photograph:
M. Phillips).
1.4 The thermal regime of steep rock walls
The rock thermal regime is driven by the interaction of the rock surface with the atmosphere.
Hence the local surface energy balance, which controls surface temperatures, depends on the local climate, topography and surface characteristics (Hoelzle et al. 2001; Mittaz et al. 2000). The degree of complexity of energy exchange processes is determined by the heterogeneity of the rock walls.
The thermal regime of compact, near-vertical bedrock (Fig. 1.4 a) strongly depends on topographic factors, such as aspect and elevation, which modulate the magnitude of the governing energy fluxes (e.g. Coutard and Francou 1989; Gruber et al. 2004a; Mittaz et al.
2000). Net shortwave radiation is the major controlling factor of rock surface temperatures
in sun-exposed locations (e.g. Gruber et al. 2003; Mittaz et al. 2000). The MAGST is 1 to 8◦C warmer here than in shaded rock walls (Gruber et al. 2004a; Hasler et al. 2011b; PERMOS 2013). In shaded slopes the net longwave radiation and thus the air temperature mainly control the surface temperature regime, resulting in a slightly higher MAGST than MAAT (Hall and André 2001; Gruber et al. 2004a; Lewkowicz 2001; Wegmann et al. 1998). The ther- mal conditions of N facing rock walls are therefore even more sensitive to ongoing climate change (Gruber et al. 2004b; Salzmann et al. 2007), although the spatial differentiation of rock temperatures is small (Gruber et al. 2004b).
Topographically driven variability of rock surface temperature distribution was also observed in Norway by Hipp et al. (2014). Aspect dependent mean annual rock surface temperature differences were only 3◦C there. This is due to an almost complete lack of net shortwave radiation in winter at high latitudes (Hipp et al. 2014). Aspect dependent variabilities of rock surface temperatures are, however, even more amplified during the months with low sun angle in the European Alps. Here northern exposed steep slopes receive almost no direct solar radiation, whereas the shortwave radiation input is enhanced in winter on steep southerly slopes. This is caused by the low solar elevation and hence more perpendicular angle of incoming solar radiation.
Steep rock walls generally have a complex, fractured structure (Fig. 1.4 b) with varying inclination (Gruber et al. 2004a; Gruber and Haeberli 2007; Hasler et al. 2011b). Topo- graphic dependent factors driving the rock thermal regime vary significantly over short distances and are additionally influenced by varying degrees of snow and debris (Gruber et al. 2004a). This causes a spatially highly variable local surface energy balance and conse- quently complex rock thermal conditions. Therefore the small-scale variability of ground temperatures found in complex, moderately inclined terrain by Gubler et al. (2011) and Riseborough et al. (2008) may even be more pronounced in heterogeneous rock faces. Rock temperature measurements carried out by Hasler et al. (2011b) and Magnin et al. (2015a) in fractured rock walls revealed considerable deviations to measurements (Gruber et al.
2003, 2004b), as well as numerical experiments (Gruber et al. 2004a; Noetzli et al. 2007) in compact, near-vertical, snow-free bedrock. Hasler et al. (2011b) observed a decrease of both MAGST and consequently of mean annual rock temperature at 0.85 m depth by 2 to 3◦C in moderately steep (45–70◦) rock faces exposed to radiation. These authors attributed this to ventilation effects in fractures, as well as to the accumulation of a patchy and thin snow cover and its persistence during the months with most intense radiation. Magnin et al. (2015a) also assume a cooling effect of snow on sunny slopes, while in shaded slopes a snow cover exceeding 0.6 to 0.8 m is presumed to increase MAGST (Section 1.2). Therefore Magnin et al. (2015a) suggest an increase of MAGST variability between N and S facing rock walls due to the contrasting effects of the snow cover in shaded and sunny rock walls.
However, the question as to whether the influence of the snow cover on the rock thermal regime is a cooling or warming one is rather speculative.
Figure 1.4:(a) The compact, near-vertical rock wall of Pizzo Badile, Val Bregaglia, Switzerland with little to no snow (Photograph: H. Baerfuss). (b) The variably inclined, strongly fractured NW facing rock wall of Dent Blanche, Valais Swiss Alps with a highly variable snow cover distribution (Photograph: N. Wever).
1.5 Snow accumulation in steep rock walls
Recent studies based on the TLS technique (e.g. Deems et al. 2013; Prokop 2008) have shown that considerable amounts of snow accumulate permanently at every slope angle in steep, rough rock walls (Sommer et al. 2015; Wirz et al. 2011). This is a result of the particular geometry, roughness and micro-topography of the rock faces investigated. Wirz et al. (2011) identified a strong terrain-wind interaction as the main driving factor of snow distribution in a rock face and found the main snow accumulation zones on the leeward side of ridges, in small gullies or at the foot of the rock wall. In contrast, Sommer et al. (2015) suggested avalanching to be the main factor redistributing snow from extremely steep to flatter areas, thus increasing snow depth in flatter areas or at the foot of rock slopes. Hence, the snow depth distribution found in steep, rugged rock walls is spatially and temporally strongly heterogeneous. This probably results in a highly variable surface energy balance and consequently spatially variable rock temperatures. The thermal regime of steep rock faces is therefore not just modulated by aspect and slope dependent factors, but likely also by the heterogeneous distribution of the snow cover and their varying thermal properties (Smith 1975; Goodrich 1982; Zhang 2005) described in Section 1.2. Additionally, snowmelt water percolating in fractures provides an additional source of advective heat (Hasler et al.
2011a). The thermal and mechanical effects of snowmelt water infiltration in open rock joints are poorly understood in permafrost rocks, but snowmelt is often mentioned as having a significant influence on rock temperature and on slope stability (e.g. Draebing
et al. 2014; Hasler et al. 2012; Stock et al. 2013). Thus, the interaction of the variable micro- topography and micro-climate and consequently of the snow cover, as well as geological factors with the thermal regime and mechanical properties in steep rugged rock walls requires detailed investigation.
Figure 1.5:Left: Rock walls without snow or patchy snow cover. Right: the same rock walls when covered with snow. (a, b) Schwarzchopf, Eastern Swiss Alps (Photographs: A. Köhler).
(c, d) Pizzo Cengalo, Val Bregaglia, Switzerland (Photographs: SLF time-lapse camera).
(e, f ) Matterhorn, Valais Swiss Alps (Photographs: N. Griessinger, N. Wever).
1.6 Modelling permafrost distribution
Modelling the spatial distribution of permafrost is relevant for the assessment of climate change effects on natural and human systems (Section 1.1) and has been carried out for the European Alps using regionally calibrated empirical-statistical (e.g. Gruber and Hoelzle 2001; Hoelzle 1996; Imhof 1996; Keller 1992), statistical (e.g. Boeckli et al. 2012a,b; Hoelzle and Haeberli 1995; Keller et al. 1998; Gisnås et al. 2014) and physics based (e.g. Fiddes et al.
2015; Stocker-Mittaz et al. 2002) approaches at different spatial scales, varying from tens of meters to kilometres.
Traditional two-dimensional permafrost maps, based on statistical models, can serve as indicators of potential permafrost occurrence. These models have the advantage of needing only limited input variables. Air temperature, potential incoming solar radiation and precipitation are assumed to be the key variables to effectively reflect the importance of the governing physical processes influencing mountain permafrost distribution (Hoelzle et al. 2001). Local model calibrations, introduced as offset terms, are required to use statistical models. However, complex energy exchange processes at the surface and at depth are not treated explicitly (Keller 1992). The ability to represent physical processes such as snow redistribution by avalanching and wind (Hoelzle et al. 2001), 3d topographical effects in the ground (Noetzli et al. 2007) and transient changes (Harris et al. 2009) are limited. Therefore, extrapolations in time and space lead to uncertainties or misleading results (Hoelzle et al. 2001).
To capture the strong spatial variability of atmosphere–surface interactions and their modulating factors (e.g. local shading, snow) which influence subsurface properties in complex topography, high resolution physics based simulations of land surface processes are needed. The detailed description of the main energy exchange processes between the atmosphere and the surface in these approaches allows for spatial and temporal ex- trapolation and is better suited to estimate transient effects of climate change in complex mountain topography (Harris et al. 2009). However, an extensive set of input data, for example meteorological- and topographic data, as well as information of ground surface properties, are needed to apply such complex numerical models. The availability of de- tailed input data, as well as the high computational effort are a challenge for large scale simulations in complex and remote terrain (Fiddes et al. 2015). To overcome such issues, Fiddes and Gruber (2012, 2014) and Fiddes et al. (2015) developed a sophisticated model chain to estimate fine-scale permafrost distribution over large areas in heterogeneous mountain terrain. In combination with a winter precipitation correction (Schmid et al.
2012) surface and subsurface temperatures were modelled with a resolution of 30 m over the entire European Alps.
However, the 30 m grid resolution taken by Fiddes et al. (2015) cannot capture the spatial fine-scale heterogeneity of surface processes in the heterogeneous Alpine mountain system (Gubler et al. 2011; Riseborough et al. 2008), as well as in steep rock faces with complex
micro-topography (Hasler et al. 2011b; Magnin et al. 2015a). The coarse grid resolution cannot account for local phenomena, such as topographically driven snow accumulation and its redistribution by wind and gravitation (Bernhardt and Schulz 2010; Gruber 2007;
Mittaz et al. 2002). In addition, it is impossible to account for spatially variable lateral heat fluxes at the debris- or rock surface (Mittaz et al. 2000), as well as between mountain sites (Gruber et al. 2004c; Wegmann et al. 1998), which cause strongly variable ground surface temperatures leading to substantial 3d thermal effects at depth of steep rock walls with convex topography (Noetzli et al. 2007; Noetzli and Gruber 2009). These authors applied a model chain combining atmospheric and surface (TEBAL: Gruber 2005; Stocker- Mittaz et al. 2002), as well as subsurface processes (FRACTURE: Kohl and Hopkirk 1995) to investigate differing governing processes of ground heat transport in steep, convex topography. Complex 3d patterns of ground temperature and heat flow density below steep, exposed bedrock topography were modelled. Here, the heat flux is directed from the warmer to the colder mountain side, resulting in permafrost degradation from different sides in steep rock walls, but also in permafrost occurrence in many locations where rock surface temperatures would not suggest the presence of permafrost. The subsurface temperature regime of steep rock walls is controlled by spatially varying surface temperatures and is hardly influenced by the geothermal heat flux (Noetzli et al. 2007). In process based energy balance models, the common assumption of 1d vertical heat conduction in the ground (e.g.
Fiddes et al. 2015; Gruber 2005; Lehning et al. 2002a; Luetschg et al. 2003; Stocker-Mittaz et al. 2002) is therefore a poor description for modelling rock temperatures and permafrost distribution in steep rock walls, which are often subject to deeper thaw than flatter areas where 1d warming below the ground dominates (Noetzli et al. 2007).
However, before a suitable investigation of 3d subsurface heat flow is possible in steep bedrock, the strongly variable spatial and temporal ground surface conditions and its modulating factors need to be modelled realistically and with high spatial resolution. One of these modulating factors is the snow cover. In the past a lack of snow in steep rock walls exceeding 50◦was generally assumed for modelling purposes (Fiddes et al. 2015; Gruber et al. 2004a; Mittaz et al. 2002; Noetzli et al. 2007; Pogliotti 2011), due to the assumption of snow removal from steep rock by wind and gravitational transport of snow by avalanching or sloughing (Blöschl and Kirnbauer 1992; Gruber Schmid and Sardemann 2003; Winstral et al. 2002).
We argue that rock temperature modelling without snow in steep rock walls is erroneous, since the snow cover is the most important factor controlling the ground thermal regime (Section 1.2). Thus, the implementation of the snow cover in energy balance models and its redistribution by wind (Mott and Lehning 2010) and avalanches (Gruber 2007) is urgently required, although it is a challenging issue (Fiddes et al. 2015).
1.7 Research goals and open research questions
The fundamental processes of snow–ground temperature interaction in steep rock walls must be understood to improve current permafrost distribution models, as well as knowl- edge of mechanical effects of the snow cover on rock wall stability. This thesis aims to quantify strongly variable snow depth distribution on steep, rough rock walls. In order to identify the thermal influences of the snow cover in steep bedrock the thermal reactions close to the rock surface (0.1 m depth) are measured and analysed at different temporal scales. Preferential patterns of snow accumulation can most likely be found in steep rock with temporal varying influences on the rock thermal regime. To accurately investigate the ground temperature distribution and possible permafrost occurrence in steep rugged rock walls, modern state-of-the-art numerical models simulating the snow cover on steep rock are required. The interaction between snow depth distribution, snow cover duration and the rock thermal regime is described for small spatial and temporal scales using a combination of a precipitation scaling approach and a distributed process based energy balance model that describes both snow cover and ground temperature evolution. This is necessary since current snow and energy balance models have limitations in describing the spatial variability of the snow cover and its influence on ground temperatures at small scales. Further knowledge on the mechanical effects of snowmelt water infiltration in rock joints on rock wall stability is required in the context of natural hazard management.
To achieve these aims detailed investigations are carried out at two field sites with differing topographic-, climatic- and thermal conditions, as well as mechanical properties:
the Gemsstock ridge, central Swiss Alps and the Steintaelli ridge, Valais Alps.
The following five research questions are the basis of this thesis. They address open questions regarding snow cover distribution in steep rough rock walls and its influence on the ground thermal regime and slope stability.
• Can a seasonal snow cover accumulate in steep rock walls?
• What are the influences of changing surface characteristics such as the snow cover on the thermal conditions in steep bedrock permafrost?
• Can snow–rock temperature interactions in very steep terrain be simulated by the existing snow cover models SNOWPACK and Alpine3D?
• What are the thermal effects caused by neglecting the accumulation of a snow cover in steep bedrock?
• What is the mechanical response of fractured rock to the accumulation and melt of a snow cover?
1.8 Outline of this thesis
The aims of this thesis are to I) quantify the accumulation of a seasonal snow cover in steep, rough rock walls, II) measure snow depth distribution and its influence on the rock thermal regime, III) test the applicability of a state-of-the-art 1d energy balance model to accurately model snow depth and consequently rock temperatures at the surface and at depth in steep rock walls and IV) extend the 1d model approach to a 3d approach applied at the local rock wall-scale with high spatial resolution. The thesis is divided into four main chapters, corresponding to three peer reviewed, published journal articles (Chapters 2, 3 and 5:
Haberkorn et al. 2015a,b; Draebing & Haberkorn et al. 2016) and a paper in revision (major revisions) (Chapter 4: Haberkorn et al. 2016). A published co-authored paper (Phillips et al.
2016) is included in the Appendix A1.The above mentioned topics will be addressed as follows:
Chapter 2:Snow as a driving factor of rock surface temperatures in steep rough rock walls.
Here the impact of the spatially variable snow cover on the thermal regime of steep per- mafrost rock walls is investigated. The small-scale variability of the snow depth is quantified and aspect dependent snow cover characteristics are discussed with regard to their insulat- ing properties.
Chapter 3:Thermal regime of rock and its relation to snow cover in steep Alpine rock walls:
Gemsstock, central Swiss Alps.
In this study the applicability of the 1d physics based energy balance model SNOWPACK is tested to simulate the seasonal snow cover and its effects on the thermal regime of two points in a N and S facing, steep rock wall. In order to provide detailed and correct meteo- rological input data to the model, parametrizations of incoming shortwave and longwave radiation are introduced. The influence of the measured and modelled feedback between the snow cover and the thermal regime at a homogeneous and a heterogeneous rock wall location are discussed.
Chapter 4: Distributed snow and rock temperature modelling in steep rock walls using Alpine3D.
In this study the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on the rock thermal regime in rugged, steep rock walls is modelled. Based on the 1d model described in Chapter 3, the model used here is the distributed physics based energy balance model Alpine3D in combination with a precip- itation scaling method to introduce varying snow distribution. As this is a first attempt to model the snow cover and its influence on the ground in a steep rock wall using this approach, a sensitivity study for virtual snow-free scenarios was also performed in order to assess the bias induced by neglecting the snow in steep bedrock in recent permafrost
modelling studies. Further the performance of Alpine3D, its limitations and uncertainties are discussed and evaluated against a dense network of validation data.
Chapter 5:Thermal and mechanical responses resulting from spatial and temporal snow cover variability in permafrost rock slopes, Steintaelli, Swiss Alps.
The heterogeneous distribution of snow in rugged rock slopes and its influence on per- mafrost and rock stability are investigated at the Steintaelli study site. Here, unstable rock conditions are observed and monitored, before the actual occurrence of failure. To evaluate the effects of the intermittent snow cover on the rock thermal regime and on mechanical properties a multi-method approach is applied combining near-surface rock temperature measurements, seismic refraction tomography, 1d rock temperature modelling, as well as crackmeter measurements.
Chapter 6 merges all aspects treated in this thesis in an overall discussion, while Chapter 7 finally summarize the conclusions from these studies and Chapter 8 gives an outlook into possible future approaches regarding snow–rock temperature modelling of steep rock walls in permafrost research.
Appendix A1:Seasonally intermittent water flow through deep fractures in an Alpine rock ridge: Gemsstock, central Swiss Alps.
This co-authored research was performed during this PhD study and focuses on obser- vations of water flow in rock wall joints and their possible mechanical impact on rock wall stability. Brief thermal anomalies measured in the Gemsstock borehole indicate that summer precipitation penetrates rock wall joints. In contrast there is a lack of thermal anomalies during spring snowmelt due to the presence of a basal ice layer at the end of win- ter. The mechanical impact of melt water infiltration is therefore assumed to be negligible at this site.
temperatures in steep rough rock walls
Haberkorn, A., Hoelzle, M., Phillips, M., and Kenner, R. Snow as a driving factor of rock surface temperatures in steep rough rock walls.Cold Regions Science and Technology, 118:
64–75, 2015a. doi: 10.1016/j.coldregions.2015.06.013
Abstract
Observations show that considerable amounts of snow can accumulate in steep, rough rock walls. The heterogeneously distributed snow cover significantly affects the surface energy balance and hence the thermal regime of the rock walls.
To assess the small-scale variability of snow depth and rock temperatures in steep north and south facing rock walls, a spatially distributed multi-method approach is applied at Gemsstock, Switzerland, combining 35 continuous near-surface rock temperature mea- surements, high resolution snow depth observations using terrestrial laser scanning, as well as in-situ snow pit investigations.
The thermal regime of the rock surface is highly dependent on short- and longwave radiation, albedo, surface roughness, snow depth and on snow distribution in time and space. Around 2 m of snow can accumulate on slopes with angles up to 75◦, due to micro- topographic structures like ledges. Hence, contrasts in mean annual rock surface tempera- ture between the north and the south facing slopes are less than 4◦C. However, significant small-scale variability of up to 10◦C in mean daily rock surface temperature occurs within a few metres over the rock walls due to the variable snow distribution, revealing the hetero- geneity and complexity of the thermal regime at a very local scale. In addition, multiple linear regression could explain up to 77% of near-surface rock temperature variability, which underlines the importance of radiation and snow depth and thus also of the topogra- phy.
In the rock faces the thermal insulation of the ground starts with snow depths exceeding 0.2 m. This is due to the high thermal resistance of a less densely packed snow cover, especially in the north facing slope. Additionally, aspect induced differences of snow cover characteristics and consequently thermal conductivities are observed in the rock walls.
2.1 Introduction
Pronounced rock fall activity has been observed in Alpine permafrost regions over the last decades (Deline et al. 2012; Gruber et al. 2004b; Gruber and Haeberli 2007; Ravanel and Deline 2011). Rock slopes containing ice are sensitive to ongoing climate change (IPCC 2013). Rising air and rock temperatures can reduce the effective thermal stress in ice filled rock joints (Haeberli et al. 1997), enhancing destabilization and possibly leading to rock slope failure (Krautblatter et al. 2013). Rock temperature data are therefore essential for a detailed understanding of thermo-mechanical processes in rock walls, and to model and predict permafrost related hazards (Krautblatter et al. 2012), which may threaten human lives and infrastructure in the densely populated Alps.
Snow has a thermal influence on the ground due to its low thermal conductivity (Fierz and Lehning 2001), high surface albedo and the consumption of energy during snowmelt (Sturm et al. 1997; Zhang 2005). The snow cover either has a warming or cooling effect on ground temperatures, depending mainly on the snow depth (Luetschg et al. 2008; Phillips and Schweizer 2007), as well as on its initial timing and duration (Hoelzle et al. 2003; Zhang et al. 2001). A thick snow cover (> 0.6 m) decouples the rock surface from the air temperature due to increasing thermal insulation with increasing snow depth (Keller and Gubler 1993), resulting in an increased MAGST (Keller and Gubler 1993; Luetschg et al. 2003; Matsuoka and Sakai 1999; Zhang et al. 2001). In contrast, a thin (< 0.15 m) and patchy snow cover leads to ground cooling due to an increase in longwave emissivity and albedo at the surface, in combination with a low thermal resistance of the thin snow cover (Keller and Gubler 1993;
Luetschg et al. 2008). Snow depth and its distribution therefore influence the existence of permafrost in both gently inclined slopes and in steep rock faces. The occurrence of snow in steep rock walls has been confirmed by Wirz et al. (2011), a possible influence of snow on spatially distributed rock surface temperatures is discussed by Magnin et al. (2015a), as well as Hasler et al. (2011b). Haberkorn et al. (2015b) provide a first quantitative (measured and modelled) investigation of the effects of snow on rock thermal processes.
To account for the complexity of rock walls and their thermal conditions, spatially dis- tributed rock temperature measurements in various types of rock walls covering different aspects are necessary. Measurements in compact, near-vertical and also snow-free rock (Gruber et al. 2003), as well as distributed surface energy balance modelling (Gruber et al.
2004a; Mittaz et al. 2000; Noetzli et al. 2007) to extrapolate rock thermal conditions in space and time underline the dominance of topography on permafrost distribution in steep bedrock. These authors found 7 - 8◦C warmer MAGST in Alpine rock faces exposed to solar radiation than in shaded ones. The assumption of a lack of snow in rock walls ex- ceeding 50◦due to gravitational processes such as avalanching and sloughing (Blöschl and Kirnbauer 1992; Seligman 1936; Winstral et al. 2002) is not applicable for rough rock walls with a complex micro-structure (Haberkorn et al. 2015b). This implies that the thermal regimes observed and modelled in the idealized case of vertical compact rock are different.
Hasler et al. (2011b) reported a likely reduction of MAGST of 2 - 3◦C in moderate to steep (45◦- 70◦), fractured rock faces exposed to solar radiation. This is assumed to be due to the accumulation of snow persisting during the months with most intense solar radiation. The thermo-insulating effect of snow accumulating locally in steep rock is also addressed by Magnin et al. (2015a). For a thick snow cover (> 0.6 - 0.8 m) the latter observed a MAGST increase in shaded areas comparable to that in gentle mountain slopes and in contrast, a MAGST decrease in sun-exposed faces due to the higher surface albedo of snow, thus reversing the thermo-insulating effect of thick snow.
The snow cover influences the rock surface energy balance, due to changes in both the radiation budget and the turbulent fluxes of sensible and latent heat at the rock surface (Armstrong and Brun 2008). Although Hasler et al. (2011b) and Magnin et al. (2015a) assume reduced MAGST differences between north and south facing rock walls due to the accumulation of snow on micro-reliefs, HS are only estimated in these studies and are described qualitatively in terms of “thin” or “thick” snow accumulations rather than quantitatively. High quality snow depth and snow characteristic data in combination with rock temperature measurements are therefore required to better quantify the impact of the snow on the rock thermal regime.
High resolution TLS is suitable to measure snow depths accurately and to determine the spatial distribution of snow, both in gently inclined slopes (Deems et al. 2013; Grünewald et al. 2010; Prokop 2008) and in steep, rough rock walls (Haberkorn et al. 2015b; Wirz et al.
2011). The accumulation of considerable amounts of snow (2 - 3 m) in slopes between 70◦ to even 90◦due to local micro-topographic asperities was observed by (Haberkorn et al.
2015b). The heterogeneous spatial distribution of the mountain snow cover (e.g. Pomeroy and Gray 1995; Seligman 1936) is mainly attributed to the deposition and redistribution of snow due to wind (Lehning et al. 2008; Schweizer et al. 2008; Trujillo et al. 2007; Wirz et al. 2011), to micro-topographic properties such as terrain roughness, terrain concavity and distance to underlying ledges (Haberkorn et al. 2015b; Magnin et al. 2015a) and to spatially varying ablation processes due to local radiation (Mott et al. 2011) and shading from surrounding terrain.
To assess the impact of the heterogeneously distributed snow cover on the strong small- scale variability of NSRT, steep north and south facing rock walls were investigated over a period of 2 years at Gemsstock, Swiss Alps. The sectors of the rock walls where snow can or cannot accumulate are characterized and the thermal response of the rock is analyzed. To do this, we applied a spatially distributed multi-method approach with a high temporal and spatial resolution. This involved combining 35 continuous NSRT measurements, remote observations of the snow depth and its distribution using TLS and in-situ snow cover observations (snow pits) at different stages over two consecutive winters.The dependence of the surface offset and consequently of NSRT on air temperature, snow depth, terrain roughness and PISR is investigated using multiple linear regression and is discussed in the
context of the heterogeneous and complex processes occurring in steep mountain rock walls.
2.2 Study site
The study site is part of the Gemsstock mountain ridge (46◦36’ 7.74" N; 8◦36’ 41.98" E;
2961 m a.s.l.), located above Andermatt, central Swiss Alps (Fig. 2.1). The rocky flanks of the ridge investigated face north-west and south-east and are subsequently simply referred to as the N and S slopes. The 40 m high slopes are 40◦to 70◦steep, with vertical to overhanging (> 90◦) sections and extend from an elevation of 2890 m a.s.l. to 2930 m a.s.l.. The ridge has a width of 40 m at its base, and tapers off towards the top. The whole N facing scarp slope and the upper part of the S facing dip slope consist of bare Gotthard paragneiss and granodiorite, with quartz veins, whereas the lower half of the southern slope is partly covered by patches of grass and moss. On the local rock wall-scale micro-topographic contrasts dominate the N face with a series of practically horizontal ledges intersecting the rock wall, which correspond to joints striking southwards at 70◦and alternating with steep to vertical parts. In contrast, the S facing dip slope is rather smooth and homogeneous (Fig. 2.2).
Gemsstock is located directly on the main divide of the Western Alps, and is thus affected both by northerly and southerly airflows. Meteorological data are obtained from an on-site AWS located at the northern foot of the rock wall (Fig. 2.1). Meteorological differences to surrounding AWS at lower elevations (Gütsch, 2287 m a.s.l., 6 km north of Gemsstock;
Urseren, 2170 m a.s.l., 8 km west of Gemsstock; Bedretto, 2450 m a.s.l., 11 km south-west of Gemsstock) are clearly reflected in the enhanced orographic precipitation from the north and the south at Gemsstock. Maximum snow depths are 4.5 m at the AWS Gemsstock compared with 3.5 m at the close by AWS Gütsch (Haberkorn et al. 2015b). Prevailing wind directions are from north-east to north-west, but also from the south during föhn storms.
The MAAT measured at the AWS Gemsstock was - 2.6◦C during the study period between 1 August 2012 and 31 July 2014. The year 2012-2013 was 1◦C warmer than 2013-2014 (Table 2.1), although the mean winter air temperature (December to February) in 2012- 2013 was 3.6◦C colder. Nevertheless, both years were warmer (1.0 to 1.5◦C) than the MAAT in the reference period 1981-2010 measured at the MeteoSwiss AWS Gütsch. The snow cover development during the two particularly long and snow rich winters was relatively similar at Gemsstock (Fig. 2.3), although most snowfalls in the year 2012-2013 were dominated by northerly airflow, in contrast to 2013-2014, when snowfalls were dominated by southerly airflow, as shown by data from neighboring AWS. However, initial and maximum snow depths were lower in winter 2013-2014 and hence the timing of snow disappearance differed between the two winters.
Borehole temperatures measured continuously since 2005 in a horizontal borehole
