Modeling the water potential of snow accumulations on Mount Aragats, Armenia

Transcription

1 University of Fribourg Department of Geosciences Master Thesis Modeling the water potential of snow accumulations on Mount Aragats, Armenia by Alexander Nestler Supervision Dr. Matthias Huss Prof. Dr. Martin Hoelzle Fribourg, October 2, 2013

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3 Abstract On Armenia s highest peak, Mount Aragats, large, wind-driven snow accumulations are covered with Geotextiles in order to delay snow melt and to provide additional water in the drier period of the year. This study proves the favorable effect Geotextiles have upon the redistribution of seasonal runoff from snow covered areas. When uncovered snow areas have almost disappeared textile-covered areas are shown to still provide runoff which is up to 5 times higher. However, it emphasizes the necessity on a larger scale in order to achieve a significant effect. Results are calculated both with an energy-balance model and with a distributed temperature-index model. Both models coincide in their statement about the efficiency of Geotextiles. Nevertheless, extrapolations to higher altitudes yield quite diverging results, attributing a better performance to Geotextiles using the temperature index model. With three distinct approaches an additional attempt was made to calculate snow volumes on Mount Aragats. However, differences between the approaches were big, uncertainties large and volume calculations remained nothing but rough estimations. In spite of the uncertainties this study provides insight into the processes of wind-driven snow redistribution and the nature of snow melt in high mountain regions and helps to estimate the effect of snow preserving methods, which are performed in the context of the Swiss- Armenian FREEZWATER Project. I

4 Acknoledgements I would like to thank all the people and institutions who contributed to this project and thus to the realization of my master thesis. I would particularly like to thank Dr. Matthias Huss for the patient supervision of my thesis, his valuable suggestions, his help and input during field work but also for the opportunity to participate in this project. Furthermore, I am very grateful for the help and support Sandra Mohr gave me during field work and beyond. I express my deepest gratitude to the Cage Holding Sa, Switzerland for their financial support of the FREEZWATER Project. Anna Ambartsumian and Flavio Santi are thanked for their commitment in this respect. Moreover, I am thankful for the support I received in Armenia from the members of the FREEZWATER Project, among others Prof. Rouben Ambartsumian and Dr. Artak Hambarian. Sincere thanks are given to the American University of Armenia and the National Armenian Academy of Science for their support. I would also like to mention the Yerevan Institute of physics and its staff for providing valuable weather data and friendly accommodation at the Cosmic Ray Station Aragats. II

5 Table of Contents Abstract... I Acknoledgements... II Table of figures... VI List of tables... IX Abreviations... X 1 Introduction Theory Energy balance Long-wave radiation balance Shortwave-radiation balance Melt driven by the energy flux Melt modeling Temperature index models Energy balance models Melt reduction and frozen water storage Natural reduction of melt rates of snow and ice through debris coverage Artificial frozen storage of water Historical Review Recent studies Artificial frozen water storage Study site Introduction to the area of investigation Placement of Geotextiles and debris Particularities of snow cornices on Mount Aragats Observations of melt beneath Geotextile coverage Geology and Geomorphology Data Field Methods GPS devices GPS outline mappings Snow depth soundings Ablation measurements Snow density measurements III

6 5.2 External Data Weather data Other data ASTER GDEM Topographic map 1: LANDSAT satellite image Methods The Glacier Evolution Runoff Model Snow surface mass balance calculated with the help of the energy balance approach Snow surface mass balance calculated with the distributed temperature-index approach Snow surface albedo Subroutine: Potential extraterrestrial (solar) short-wave radiation Evaporation Runoff Routing Snow surface updating Extrapolation of results Calibration Calibration of the distributed temperature index model Calibration of the energy balance model Validation Validation of snow depth soundings Validation of the performance of GERM Results Ablation measurements Snow Density Perimeter mappings Inter-annual comparison Intra-annual comparison Volume estimations from snow depth interpolation Simulation results Modeled daily snow melt Modeled Survival of snow accumulations Modeled runoff generated from snow melt Snow distribution and volume modeling Approaches IV

7 10.2 Satellite image based volume estimation ArcGIS-based statistical distribution modeling of snow covered areas Step 1 Decreasing resolution of the DEM Step 2 Calculating curvature Step 3 Extracting positive curvatures Step 4 Skeletization Step 5 Buffering edge lines Step 6 Subtracting certain aspects Step 7 Subtracting steep slopes Step 8 Snow depth estimations Step 9 Confining snow covered areas Step 10 Decreasing snow depths at the edges Automated process-based distribution- and volume modeling of snow covered areas Input parameters Model description Validation of Snow distribution modeling Results of distribution- and volume estimations Discussion Discussion about weather data Discussion about the accuracy of ablation measurements and snow density measurements Discussion about patterns in perimeter mappings Discussion about the melt-model outputs Discussion about the efficiency of Geotextiles, their applications and alternative snow preserving methods Evaluation of Results of the snow volume modeling Conclusion References V

8 Table of figures Fig. 2.1 Most relevant energy fluxes determining the energy balance on a glacier... 2 Fig. 2.2 Surface albedo in dependence of wave lengths of electromagnetic radiation... 2 Fig. 2.3 Selected surface albedo on a glacier... 3 Fig. 2.4 Relationship between positive degree-days and ice ablation... 7 Fig. 2.5 Potential clear-sky solar radiation compared to global radiation... 8 Fig. 2.6 variations of potential clear-sky direct solar radiation... 9 Fig. 3.1 Variations of measured mean daily ablation and calculated thermal resistance with respect to debris thickness Fig. 3.2 Debris keeping underlying snow from melting Fig. 3.3 Surface loss from ice test plots with different protective coverings Fig. 3.4 Thermal conductivity of different snow types at different densities Fig. 3.5 Ablation regarding snow and ice melt comparing different technical measures Fig. 3.6 Covered test area just after the removal of the Geotextile Fig. 3.7 Series of low walls damming water to form an artificial glacier Fig. 3.8 Artificial glacier created on Mount Aragats Fig. 4.1 Climate diagram from Erivan Fig. 4.2 Geographical situation of Armenia Fig. 4.3 Snow cornice along the Shampoor ridge Fig. 4.4 Overview of the area of investigation Fig. 4.5 Studied snow cornices Fig. 4.6 a Location of melt-reducing measures Fig. 4.7 Snow covered areas along Shampoor ridge Fig. 4.8 Snow cliff. Maximum snow thicknesses of 5 m Fig. 4.9 Recurring patterns of snow accumulation Fig. 4.10a Winter layers Fig. 4.10b Rupture of snow accumulations Fig Melt corridors Fig Snow-cornice terraces Fig decay of Geotextile covered areas Fig The Turkish-Iranian plateau and Pliocene-Quaternary centers of volcanism Fig Lateral moraines in the central crater of Aragats Fig Polygon soils Fig Rock glacier Fig Volcanic Boulders Fig System of Canyons around the central crater Fig Firn field evolved from a snow cornice Fig. 5.1 Location of glaciological measurements and protective measures Fig. 5.2 Mapping of snow cornices Fig. 5.3 Snow depth sounding Fig. 5.4 Ice layers Fig. 5.4 Ablation measurements Fig. 5.5 Snow pit VI

9 Fig. 5.6 Cylinder with a defined volume to infer snow density Fig. 5.7 Automated weather station Fig. 5.8 Daily average readings Fig. 5.9 Distribution of wind directions Fig Wind directions in winter Fig Wind directions in summer Fig. 5.12a Diurnal average wind directions and wind velocities in August Fig. 5.12b Diurnal average wind directions and rain rate in August Fig. 6.1 Schematic overview of GERM Fig. 7.1 Calibration I of the Degree-Day Model Fig. 7.2 Calibration II of the Degree-Day Model Fig. 7.3 Calibration of the energy balance model Fig. 8.1a Interpolated snow depth soundings on 17 June Fig. 8.1b Interpolated snow depth soundings on 22 and 23 June Fig. 8.2 Interpolated snow depth of 2012 and 2013 in comparison Fig. 8.3a Comparison of perimeters Fig. 9.1 Cumulative snow melt in June and July Fig. 9.2 Cumulative snow melt in June Fig. 9.3 Average daily melt rates in June and July Fig. 9.4 Cumulative snow melt at stake STA Fig. 9.5 Snow densities in Fig. 9.6 Snow densities in Fig. 9.7 Comparison of perimeters of Level 01 mapped in 2011, 2012 and Fig Perimeter of snow covered area at the Vishab site on several dates during the year Fig Perimeter of snow covered area Level 01 on several dates during the year Fig GERM 3 dimensional output Fig Daily air temperatures and daily melt calculated at 3200 m a.s.l. by the temperature-index model for an uncovered andcovered snow surface Fig Daily air temperatures and daily melt calculated at 3200 m a.s.l. by the energy-balance model for an uncovered and covered snow surface Fig Daily melt rates at 3200 m a.s.l. calculated with the temperature-index model and the energy-balance model Fig Daily melt rates at 3950 m a.s.l. calculated with the temperature-index model and the energy-balance model Fig Survival of snow masses Fig Survival of snow masses Fig Daily runoff simulated for textile coverage with the energy-balance model and the temperature-index model Fig Daily runoff at 3200 m a.s.l. simulated for covered snow surfaces and uncovered snow surfaces Fig Runoff in 2011 and Fig LANDSAT scene showing snow covered areas Fig Snow covered areas and bare ground Fig Average snow depths with distance from the edge Fig Curvatures from ASTER GDEM above 2500 m Fig Edge lines obtained by skeletization of positive curvatures VII

10 Fig General regression function calculated for the Change in snow cover with altitude Fig Regression function calculated for the change of snow cover below 2900 m Fig Buffer around edges Fig. 10.9a Mask to exclude certain aspects Fig. 10.9b Buffer without excluded aspects Fig Final mask of constraints for areas that are likely to hold snow Fig Orientation of a relative number of snow pixels in different altitudinal belts Fig Weightening of snow accumulation in dependance of slope angles Fig Snow covered area classified form LANDSAT image in comparison with automated approach after 50 days of melt and ArcGIS-based approach Fig Snow covered area within the area of investigation classified form LANDSAT image in comparison with automated approach after 50 days of melt and ArcGIS-based approach Fig Modeled snow depths based on a satellite image Fig ArcGIS-based modeled snow depths Fig Modeled snow depths based on the automated approach Fig Daily runoff and precipitation rates Fig Difference between runoff from covered and uncovered snow accumulations Fig Hypothetical skiing piste VIII

11 List of tables Tab. 3.1 Favorable characteristics of Geotextiles with respect to snow protection Tab. 5.1 Overview of Measurements performed within the area of investigation Tab. 7.1 Parameter configurations for calibration I with wrong precipitation data Tab Maximum slope inclination for snow accumulation Tab Maximum snow depths in different altitudinal belts Tab Snow volumes calculated by different approaches in comparison IX

12 Abreviations C D DDF DL E E M m s n ɸ Q SH SWE t W W max y β φsun Water added to the slow storage Cumulative mass balance Long-wave radiation factor Long-Wave radiation factor, linearize around the freezing point Snow redistribution factor Potential Evapotranspiration Latent heat flux Sensible heat flux Solar constant Latent heat of fusion for Long-wave incoming radiation Long-wave outgoing radiation Melt beneath coverage Mean air pressure at sea level Solid precipitation Liquid precipitation Precipitation at a reference station Average earth sun distance Empirical factor according to surface type Volume of water in a reservoir Gauge under catch correction factor Saturation vapor pressure Retention constant, i.e. time it takes to empty a reservoir Radiation coefficients for fresh snow (fsnow) and old snow (osnow) Distinction factor between snow and rain Atmospheric transmissivity factor (clear-sky conditions) Density of snow Density of water Change in the reservoir volume (of a reservoir r) Cloud factor Positive degree days, i.e. sum of positive daily mean temperatures Degree-Day factor, i.e. melt per D Fraction of daylight per day Surface energy flux Actual evapotranspiration Altitude Snow melt Surface melt, measured Discrete number of time steps Shading angle Runoff Snow height Snow water equivalent Time step Buffer width around edges Maximum buffer width Constant reduction factor for snow coverage Slope angle Azimuth of the sun X

13 Aspect of the slope Hour angle at sunset - Hour angle at sunrise Ground heat flux Latent heat Melt factor Mean air pressure at the altitude of the calculation Incoming solar radiation Current earth-sun distance Air temperature Day of the year Zenith angle Reduction factor accounting for filtering of solar radiation by clouds and atmospheric particles Surface albedo Declination of the sun Impact angle of solar radiation on a surface Stefan-Boltzmann constant (5.67 x 10-8 J m -2 K -4 s -1 ) φ Geographical latitude Hour angle Emission coefficient Net energy flux Φslope XI

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15 1 Introduction 1 Introduction Mount Aragatz Armenia s highest mountain is subjected to large quantities of snow every winter. These snow accumulations are redistributed by strong winds and are deposited on the lee side of sharp edges in topography as snow cornices. These cornices persist when most of the general snow cover has disappeared in early summer. They still provide melt water on a regional scale when temperatures rise and amounts of precipitation drop. Nevertheless, by the arrival of the driest period, in mid-august, the snow cornices have already disappeared. All streams descending from Mount Aragatz dry up as they all are fed by melt water. Since the underground mainly consists of large volcanic boulders with a high permeability for water these streams dry up in a matter of days leaving the Aragatz region with a severe water deficiency. Against this background, members of the Swiss-Armenian joint venture FREEZWATER cover those wind-driven snow accumulations in an attempt to relent their melting sufficiently to preserve them long enough to provide melt water when it is most needed. This study scientifically accompanies an experimental coverage of one particular snow cornice with Geotextiles. In three extensive field visits to Mount Aragatz between 2011 and 2013 glaciological measurements were performed in order to estimate the efficiency of the application of Geotextiles. The effect Geotextiles have upon the melt of snow accumulation was already intensively studied by OLEFS and FISCHER 2008 and OLEFS and LEHNING These studies consider 2 dimensions: the temporal evolution of snow accumulation at one point. By contrast this study represents a dynamic regard on 3 dimensional snow accumulations and specifically looks at runoff resulting from the melting snow. It represents an attempt to quantify runoff from snow accumulations and the role Geotextiles play in the generation of runoff. In order to facilitate the planning of protective measures snow accumulations are simulated with regard to their longevity under different scenarios. The simulated scenarios include different altitudes and years with- and without Geotextile coverage. For this purpose a modified version of the numerical model GERM as presented in HUSS et al was used. GERM (Glacier Evolution Runoff Model), in its original form, was designed to simulate the evolution of Mountain Glaciers and their runoff during the melt season. In this study GERM was adapted to snow accumulations both in terms of the size of the studied object and temporal resolution. With the help of GERM both the spatial evolution and the generated runoff was both calculated based on an energy-balance approach, proposed by OERLEMANS 2001, as well as a distributed temperature-index approach as presented in HOCK Both approaches were compared and thus provided not only a validation of results but also defined a range of possible simulation outcomes. Furthermore LANDSAT satellite imagery was used to assess the spatial distribution of snow in the whole Mountain area. For an application in mountainous areas where no adequate satellite imagery is available the distribution of snow was assessed using two different models. These models involved statistical as well as process-based approaches and additionally provided a rough estimation of the total snow volume on Mount Aragats at a given time. In brief this study will on the one hand help to guide and evaluate the activities performed in the context of the FREZZWATER Project. It will on the other hand also help to gain insight into processes governing the formation of wind-blown snow accumulations and the nature of snow melt in high mountain regions. 1

16 Albedo 2 Theory 2 Theory 2.1 Energy balance Melt processes on snow or ice surfaces are governed almost exclusively by the intensity of energy fluxes (OERLEMANS 2001). Their schematic interactions are represented in Fig Fig. 2.1 Most relevant energy fluxes determining the energy balance on a glacier (Adopted from OERLEMANS 2001, P. 16) According to OERLEMANS 2001 radiation fluxes are the energy components with the biggest impact on melt process, namely solar radiation (short-wave radiation) and long-wave radiation. Especially on snow surfaces the bigger part of the incoming solar radiation is reflected back into the atmosphere depending on the surface albedo. The albedo itself is a highly variable factor describing a surface s reflectance properties. In the case of snow the albedo may vary after OERLEMANS 2001 as a function of the crystal structure, the morphology of the surface, the degree of contamination of the surface with soot or dust, liquid water in the porous space and at the snow surface, the elevation of the sun, cloudiness, etc.. Furthermore the reflectance capacity of a surface is everything but uniform for all wavelength. After O BRIEN and MUNIS 1975 snow reflects most radiation within the visible electromagnetic spectrum below wavelengths of 0.8 µm. Beyond this value snow tends to absorb more and more radiation and reflects less. A modeling attempt by WISCOMBE and WARREN 1980 illustrates the dependence of albedo for different kinds of snow (Fig. 2.2). Fig. 2.2 Surface albedo in dependence of wave lengths of electromagnetic radiation. Modeled vs. observed (adopted from: WISCOMBE and WARREN 1980, p.: 2722) In the face of this multitude of influences an exact determination of the surface albedo is fairly impossible (OERLEMANS 2001). For this reason most models operate with fixed albedo values for different types of surface material, ranging from fresh snow with the highest surface albedo over old snow and bare ice to debris with the lowest reflectance. Fig. 2.3 illustrates the great variety of surface albedos on a glacier. 2

17 Old clean wet snow Old debris-rich dry snow Debris-rich ice Clean ice surface albedo Fresh dry snow Old clean dry snow Superimposed ice Blue ice 2 Theory For long-wave radiation ice and snow surfaces have an absorption and reflection capacity of close to one (OERLEMANS 2001). This means that almost all of the radiation in the longwave spectrum is absorbed and the same amount is emitted, although OERLEMANS 2001 claims the emitted portion to be slightly higher. Hence the long-wave balance on glaciers or on snow fields is slightly negative for most of the time. Nevertheless, on some occasions, e.g. during overcast conditions with warm and humid air, the long-wave energy input from the atmosphere may be also positive. Clouds play an important role for a surface s energy budget. However, their influence on the different wave-lengths is not uniform. The lesser the cloud cover the more short-wave radiation and less long-wave radiation reaches the surface and vice versa. Though, of less importance than the radiation fluxes, turbulent fluxes of sensible and latent heat play quite a substantial role in the energy balance of a glacier or snow surface. For positive air temperatures sensible heat fluxes are directed towards the snow or ice surface (OERLEMANS 2001). By contrast latent heat fluxes may have a trajectory in both directions which are mostly defined by air moisture. In terms of energy fluxes precipitation both liquid and solid plays a minor role according to OERLEMANS Depending on its temperature compared to the glacier surface precipitation has the capacity to either remove or import energy. There are several processes influencing the energy balance inside a glacier. The only process, however, of significant importance is the percolation of melt water from the surface through the snow and firn (OERLEMANS 2001). It imports energy in the form of latent heat as soon as the water refreezes inside the snow or firn pack. Molecular conduction is the only active process for bare ice Fig. 2.3 Selected surface albedo on a glacier with minimum, maximum and recommended values (based on CUFEY and PATERSON 2010, p. 146) 3

18 2 Theory Rather insignificant fluxes are those of air and water vapor convection inside the snow and firn pack. Although these fluxes are comparatively small they play a crucial role in the formation of snow crystals and the metamorphosis of snow (OERLEMANS 2001) which may alter the snow surface albedo or snow density. All above mentioned energy fluxes add up to the net energy flux after OERLEMANS 2001 as: ( ) (2.1) where Q represents the incoming solar radiation which is reflected as a function of the surface albedo α. L in and L out is a representation of the long-wave balance on the surface. The turbulent fluxes of both sensible and latent heat are expressed by H S and H L respectively. G - the ground heat flux comprises all energy transfers through convection, conduction and the penetration of solar radiation Long-wave radiation balance The long-wave radiation balance L in L out represents an important feature within the surface energy balance and thus merits some further scrutiny. The long-wave emissivity from a body can be calculated applying Stefan Boltzmann s law: According to this law the outgoing long-wave radiation from a solid body is proportional to the fourth power of its temperature T. The proportionality is expressed by the Stefan-Boltzmann constant σ, which is equal to 5.67 x 10-8 J m -2 K -4 s -1 (CUFEY and PATERSON 2010). ε is an empirical emission coefficient. A perfect emitter, i.e. a black body, has an ε of 1. According to MÜLLER 1984 ice and snow have similar long-wave emission properties as a black body. Incoming long-wave radiation is the radiation that is emitted back from the atmosphere - a process known as the greenhouse effect (CUFEY and PATERSON 2010). As the atmosphere is nothing but a radiation-emitting body Stefan Boltzmann s law can be applied here too. In this case, however, the atmosphere is not as perfect an emitter as snow or ice. It emits less radiation. Therefore ε needs to be lower. MÜLLER 1984 experimentally determined an ε between 0.5 and 0.75 for clear-sky conditions. In order to account for clouds MÜLLER 1984 introduced another empirical factor - C. The resulting formula for outgoing long-wave radiation then reads: (2.2) ( ) (2.3) For cloud-free conditions the factor C is set to 0 and increases depending on the amount and type of clouds. Since the amount of long-wave radiation reflected from the glacier surface is negligible (OERLEMANS 2001) it does not appear in this calculation Shortwave-radiation balance The energy emitted from the sun and arriving on top of the atmosphere is largely dependent on the earth s trajectory relatively to the sun. On average the amount of energy is equal to W m -2 as satellite measurements indicate (MENDOZA 2005). This value however is vastly diminished as a consequence of scattering and absorption by air molecules, gases, aerosols and ice-crystals as the solar radiation travels through the atmosphere. The solar radiation finally impinging on the earth s surface consists of direct solar radiation, scattered 4

19 2 Theory solar indirect radiation and solar radiation that is reflected from the surrounding topography. This energy flux is then termed global radiation or insolation (CUFEY and PATERSON 2010). Beside atmospheric factors solar irradiance is largely dependent on geometric factors. These are geographic latitude, the surface s exposition and shading effects of the surrounding topography (HOCK 1999, OERLEMANS 2001) Melt driven by the energy flux Once the energy budget is positive and surface temperatures of snow and ice are at 0 C melting occurs. According to CUFEY and PATERSON 2010 this situation occurs when some or all of the following conditions are fulfilled: 1 strong solar radiation (little clod cover) 2 low surface albedo (e.g.: dirty snow, shallow debris cover) 3 warm air temperatures (sensible heat flux + long-wave radiation) 4 moist air (enhances greenhouse radiation, reduction of heat losses) Baring in mind that a snow or ice surface cannot rise above 0 C and that outgoing long-wave radiation is directly correlated to temperature the energy loss caused by long-wave emission cannot be higher than at 0 C. Therefore, an increase in melt rates can only be caused by an increase in solar radiation, incoming long-wave radiation and/ or an increasing import of latent heat (CUFEY and PATERSON 2010). Mass losses may also be attributed to sublimation. In high-mountain tropical regions this effect may play an important role for (HOELZLE 2013 personal communication). 5

20 2 Theory 2.2 Melt modeling As stated in OERLEMANS 2001 mass balance modeling tries to link climatic conditions to the specific mass balance, i.e. the gain or loss of mass, of a glacier or as in this case of an accumulation of snow. The gain of mass mostly takes place through the gain of snow. As simple as it sounds it is a complex task to account for all the processes that are involved. Even the seemingly simple task of measuring snow fall is complicated by the lateral movement of snow caused by wind and makes representative measurements difficult. Hence a uniform snow cover is unlikely to be found. This applies to mountainous areas in particular. Another process which is difficult to predict is the mass gain through avalanches. This is a process which plays an important role in high mountain areas with high relief energy. The loss of mass through melt is directly linked to the fluxes in the energy balance equation 2.1 integrated over a period of time (CUFEY and PATERSON 2010). Measuring all fluxes would be a means to accurately capture snow and ice melt. However, the measurement of those fluxes is very expensive and uncertainties are big (CUFEY and PATERSON 2010). Despite the uncertainties the measurement of the most important fluxes may provide good estimates as a project by HOGG et al on the Hodges Glacier on South Georgia demonstrates. The measurements included global and reflected short-wave radiation as well as net radiation. Turbulent heat fluxes were inferred from readings of air temperature, wind speed and water vapor pressures. The latter was computed from dew point measurements. From these parameters a total melt of 3.45 m w.e. (water equivalent) could be calculated between 01 November 1973 and 04 April 1974, which almost perfectly fits the 3.37 m w.e. measured at neighboring stakes. HOGG et al found that 54 % of the melt was induced by fluxes of radiation. Sensible heat fluxes are responsible for the remaining 46 % of melt. Uncertainties as described above increase for a two dimensional modeling, for instances when it comes to modeling melt for an entire glacier (CUFEY and PATERSON 2010) or a snow field. According to CUFEY and PATERSON 2010 this is mainly due to a high spatial variability of the parameters of the energy budget such as wind, humidity, albedo or surface roughness. In the face of the almost impossible task of measuring all parameters throughout space a simplification of modeling approaches is necessary. CUFEY and PATERSON 2010 suggest relating melt to only a few or even a single parameter through empirical regressions. This considerably facilitates the extrapolation of measurements in time by using for instance temperature data of the past or climate scenarios of the future. Furthermore it allows establishing mass balances for glaciers without having to apply direct glaciological measurements. A very prominent example for using one single meteorological parameter as an indicator for melt is the degree-day factor Temperature index models Surface melt is based on the surface energy budget. However, the required data, in particular spatially distributed, is seldom available (HOCK 1999). The only parameter that is measured at every weather station is air temperature. According to OHMURA 2001 air temperature is linked to snow and ice melt mainly through long-wave radiation which is the most important source of energy contributing to melt. The importance of temperature was already realized in 1887 by FINSTERWALDER and SCHUNK who observed ablation to be proportional to temperatures above the freezing point, or more precisely to the sum of temperatures above 0 C (BRAITHWAITE 1995, HOCK 1999). This factor is termed positive degree days (CUFEY and PATERSON 2010). A widely used method is to calculate the positive degree days D as the sum of positive daily mean temperatures T j over a number of days j after CUFEY and PATERSON 2010 as: 6

21 2 Theory (2.4) Fig. 2.4 illustrates the strong linear relationship between positive degree days and surface melt. Fig. 2.4 Relationship between positive degree-days and ice ablation on different sites on the Greenland ice sheet (adopted from BRAITHWAITE 1995) A melt factor the degree-day factor now links the positive degree days to the actual snow or ice melt (CUFEY and PATERSON 2010). When surface melt m s over a given period of time is known at a certain point the degree-day factor DDF may be calculated after CUFEY and PATERSON 2010 as: (2.5) Conditioned by the relatively larger albedo of snow the DDF is usually lower for snow surfaces (HOCK 1999). Classical degree-day models in the fashion of HOCK 1999 use the following approach for the calculation of snow melt M: { (2.6) where n is the number of time steps per day (e.g. hours) and T is the air temperature at the respective time step. The main advantage of this classical method is the small number of required parameters. Nevertheless, according to HOCK 1999 classical temperature-index models feature two major disadvantages. On the one hand those models have a limited temporal resolution. The approach works well on a daily basis and is thus suited for long-term predictions. However, when it comes to e.g. capturing diurnal peaks of discharge from snow fields a daily resolution is insufficient. HOCK 1999 rather suggests the application of energy balance models for this purpose. On the other hand degree-day factors do not change in their spatial distribution, although melt rates vary significantly on a small scale. 7

22 2 Theory More sophisticated temperature-index approaches involve potential clear-sky radiation. The actual global radiation may, however, vary significantly as a function of atmospheric disturbances such as aerosols or clouds. Fig. 2.5 illustrates the deviations from the potential clear-sky radiation with measurements carried out by HOCK If such measurements exist they can be used to further enhance the performance of the model. Gs Qe Fig. 2.5 Potential clear-sky solar radiation (a) compared to global radiation (b) measured in an 1-hour interval (modified after HOCK 1999) Energy balance models As the name already suggests temperature-index models derive melt rates from indexes of the energy budget. As opposed to this, energy-balance models try to describe the whole ensemble of physical processes involved. Melt occurs as soon as the net energy budget (equation 2.1) is positive (CUFEY and PATERSON 2010). In other words superfluous energy is transferred to the snow or ice surface and is available for melting. Under the condition that surface temperatures reach 0 C melt M in mm w.e. per unit time can then simply be derived as: (2.7) where L f corresponds to the latent heat of fusion for ice and is the density of water. This method seems to be the most accurate way to simulate melt processes. Nevertheless, a large number of input parameters, which are rarely available (HOCK 1999), are required for the performance of an energy-balance model. If measurements of meteorological parameters exist they cannot be simply applied to the whole snow surface. For incoming short-wave radiation, for example, topographic corrections need to be performed as presented in GARNIER and OHMURA These corrections consider effects of shading caused by the surrounding topography as well as the orientation of the snow surface itself. Fig. 2.6 demonstrates the spatial variations of potential clear-sky direct solar radiation calculated for Storglaciären Glacier by HOCK

23 2 Theory Fig. 2.6 variations of potential clear-sky direct solar radiation in W m -2 (modified after HOCK 1999) In many cases direct measurements are not available. For this reason many energy balance parameterize many energy fluxes. Parameterizations in this context refer to estimating readings from empirical observations instead of measuring their exact value. 9

24 3 Melt reduction and frozen water storage 3 Melt reduction and frozen water storage 3.1 Natural reduction of melt rates of snow and ice through debris coverage Especially in high mountain areas with high relief-energy significant amounts of debris is transported to the snow surface (BENN et al. 2012). In all cases this layer of detritus has a significant impact on the energy balance of the glacier surface and thus influences the melt of the underlying ice and snow (KAYASTAH et al. 2000). Thin layers of debris below a thickness of 5 cm on average contribute positively to snow and ice melt (KAYASTAH et al. 2000, TAKEUCHI et al. 2000). The considerably higher albedo (KAYASTAH et al. 2000) of debris leads to a strong increase in surface temperature, which is transported through the layer as a result of conductive and convective processes (CONWAY and RASMUSSEN 2000). Convective processes in this context refer to the convection of air through the porous space. As Fig. 3.1 shows debris layers are characterized by a linear relationship between layer thickness and thermal resistivity which leads to a decrease in melt rates as layer thicknesses increase beyond a critical thickness (KAYASTAH et al. 2000). Fig. 3.2 demonstrates the effect of a sufficient debris cover on snow melt. TAKEUCHI et al report a decrease in melt rates by 40 % for layer thicknesses of 10 cm. KAYASTAH et al even state that ablation comes to a halt at layer thicknesses of more than 1 m. Fig. 3.1 Variations of measured mean daily ablation and calculated thermal resistance with respect to debris thickness (after KAYASTAH et al. 2000, p.76) At a layer thickness of about 5 cm thermal insulation outweighs the effects of the heat transport from the surface. Below a layer of this critical thickness (KAYASTAH et al. 2000) melt rates below a debris covered glacier surface are exactly equal to those of an uncovered surface. The extent of this critical thickness may, of cause, vary depending of the debris material (CONWAY and RASMUSSEN 2000). Even on Mars ice has been preserved beneath thick covers of volcanic debris as a recent study by WILSON and HEAD 2009 describes. 10

25 3 Melt reduction and frozen water storage Fig. 3.2 Debris keeping underlying snow from melting. Trient Glacier, Switzerland (photo: A. Nestler) 11

26 3 Melt reduction and frozen water storage 3.2 Artificial frozen storage of water Historical Review Long before the discussion about climate warming and the melting of glaciers emerged scientists tried to find ways to reduce the speed of the melting process. In the 60s and 70s of the 20 th century research and military stations were set up on naturally occurring ice islands and areas of sea-ice in the Arctic as well as permanently snow and ice covered areas in the Antarctic. In order to guarantee their usage throughout the entire year protective measures were developed and tested during times of high melt rates in the respective summer months (HERRMANN and STEHLE 1966). In the Antarctic the application of ice fragments or snow on the ice or snow surfaces was an efficient way to inhibit ablation, according to HERRMANN and STEHLE This method was sufficient in the relatively cold Antarctic summer. However, for the conditions during the warmer Arctic summer with temperatures alternating between above and below the freezing point a different approach was necessary. This applies even more to Mount Aragatz where summer temperatures mostly remain above 0 C. Saw dust was successfully tested by the U.S. Navy during the Olympic Winter Games in California in 1960 covering compacted snow in order to be used as parking lots. According to HERRMANN and STEHLE 1966 a 1 to 2 cm thick dust level was capable of reducing melt significantly. By absorbing water saw dust inhibits the formation of puddles which would increase the absorption of solar radiation. Laboratory tests carried out by the GOODYEAR AEROSPACE CORPORATION 1964 (in HERRMANN and STEHLE 1966) even show a surface cooling effect from wet saw dust due to evaporating water rising in capillaries through the saw dust. Since most of Armenia s vegetation is poor in trees saw dust is a scarce resource and is therefore hardly suited to be applied on Mount Aragatz. Further tests performed by the GOODYEAR AEROSPACE CORPORATION 1964 showed that aqueous (watery) Urethane foams were even more suited as a protective coverage for being an excellent insulator. In the tests 12.5 cm deep test areas of ice were covered with different types of foams, saw dust or were left uncovered. Over a period of 80 h the foams were exposed to temperatures oscillating around the freezing point and surface loss over time was observed (Fig.3.3). The comparison of the different coverings showed that a coverage with a 2.5 cm thick layer of Urethane foam performed superior to all others. The major downside to this method is a high inclination of the saw dust and the urethane foam to be blown out by wind. Fig. 3.3 Surface loss from ice test plots with different protective coverings (after Goodyear Aerospace Corp., 1964, p. 77) 12

27 3 Melt reduction and frozen water storage Recent studies Snow surface coverage with wood chips Similar to the coverage of snow surfaces with saw dust SKOGSBERG and LUNDBERG 2005 cover seasonal snow with wood chips in order to meet the need for cooling of a regional hospital in Sundsvall, Sweden. According to the authors the most important processes that contribute to the melt reduction are firstly evaporation of water that rises through the chip layer by capillary forces and secondly the insulating effect of the chip layer. According to SKOGSBERG and LUNDBERG 2005 the efficiency of the method is dependent on the wood material. E.g. dark bark has a lower albedo and, for its compactness, less water transporting capacities through capillary forces than the lighter heartwood. But in the end it is only a matter of layer thickness. According to SKOGSBERG and LUNDBERG 2005 a layer of 7.5 cm thickness is already sufficient to decrease melt rates by up to 85 %. Water injection, snow compaction and geotextile coverage A more recent comparative study by OLEFS and FISCHER 2008 looks at three diverse methods to decrease snow melt: the injection of liquid water into the snow pack, the compaction of snow and the cover of the snow surface with different materials. The liquid water injection was done with devices used for the creation of skiing pists. Pressurized water was repeatedly injected into the snowpack through a network of little cones with a spacing of 10 cm. In order to not lose mass through runoff the water was injected into a cold snow pack to allow a freezing of the water within the snow pack. The main goal of the snow compaction was to increase the snow density in order to achieve a higher thermal conductivity as shown by STURM et al (Fig.3.4), which according to OLEFS and FISCHER 2008 then leads to lower melt rates. The compaction of the snow was done with an oscillating plate on one test area and with a repeated application of moldingcutters and a finisher which is a standard procedure for the preparation of skiing pists. Fig. 3.4 Thermal conductivity of different snow types at different densities (STURM et al P.35) 13

28 3 Melt reduction and frozen water storage Any cover of a surface will have a direct impact on its energy balance. Applying coverage with suited material properties to a snow surface will diminish the energy that is available for melt (OLEFS and FISCHER 2008). The biggest effects can be achieved by placing the coverage between the onset and the end of the melt season. In the course of the study by OLEFS and FISCHER and 24 different materials were tested during the melt seasons of 2004 and 2005 respectively. These materials included among others bio-degradable materials, highly reflective tarpaulins, cotton sheets and Geotextiles. In addition, the effect of multiple layers was investigated. While both injection and compaction were found to have no substantial impact on the total ablation over the whole summer season only a surface coverage was capable of reducing melt rates significantly. Especially Geotextiles relented snow melt by 60 % slower (Fig. 3.5). Fig. 3.5 Ablation regarding snow and ice melt comparing different technical measures (adopted from: OLEFS and FISCHER 2008, p.: 380) The photograph in Fig. 3.6 demonstrates the impact Geotextile coverage has on a melting regime over one entire ablation season. A doubling of the layer of Geotextiles was reported to cause the melt rates to further decrease by 10 %. Against all expectations OLEFS and FISCHER 2008 could not observe any further decrease in melt rates by adding a further layer of textile. 14

29 3 Melt reduction and frozen water storage Fig. 3.6 Covered test area just after the removal of the Geotextile covering the snow on the Schaufelferner glacier, Austria throughout one melt season (adopted from: OLEFS and FISCHER 2008,.p: 381) From the experiments with the various cover materials OLEFS and FISCHER 2008 were able to derive those properties that increase the efficiency of their performance. Tab 3.1 shows an overview of these characteristics. Tab. 3.1 Favorable characteristics of Geotextiles with respect to snow protection after OLEFS and FISCHER 2008 radiation properties reflection of short wave-lengths high albedo emissivity of long wave-lengths thermal properties exchange of sensible heat (releasing absorbed solar energy) permeability for liquid not impermeable: formation of puddles that warm up precipitation not too permeable: protection of the snow pack against seeping rain water latent energy of fusion tensile strength to withstand strong winds sufficient surface roughness maintaining textile in position avoiding avalanches of overlying snow thickness influences the above mentioned characteristics (not linearly as shown above) The material that comes closest to these properties is the 4.5 mm thick Geotextile produced by the FRITZ LANDOLT AG. In 2010 OLEFS and LEHNING presented a study investigating melt reducing properties of this Geotextile with respect to the surface energy balance. They found that 46 % of the effect Geotextiles have upon the melting regime can be attributed to its capacity to reflect shortwave radiation. 14 % of its performance can ascribe to thermal insulation. Changes in latent heat fluxes as a result of evaporation of rainwater from the textile surface are responsible for further 10 % of its melt reducing effect. Air pockets between the textile and the snow surface are claimed to cause a further decrease in melt rates. OLEFS and LEHNING 2010 furthermore state that at lower altitudes sensible heat fluxes play a comparatively bigger role than at higher altitudes. This fact makes an application of Geotextiles in lower areas less efficient. 15

30 3 Melt reduction and frozen water storage 3.3 Artificial frozen water storage As a response to climate change many different approaches have been reported to tackle the problem of water scarcity with the creation of artificial glaciers. The creation of artificial glaciers involves the supply of liquid water and its continuous freezing. In Ladakh, India a glacier was tried to be built as presented in NORPHEL Many Nepalese villages have long been supplied with fresh water from melting glaciers. With the glacier retreat this supply is not always given anymore. A series of low walls as shown in Fig. 3.7 to dam water from a small creek is now supposed to take the place of natural glacier. In the upstream section of these small dams water is supposed to freeze in winter time to form shallow accumulations of ice. Fig. 3.7 Series of low walls damming water to form an artificial glacier in Ladakh, Nepal (adopted from: NORPHEL 2009, p.: 63) In two different and independent approaches ice is attempted to be accumulated by means of water spray. In the Black Forest, Germany a system of lawn sprinklers was used to spread water over a wide area to add layer after layer to a growing artificial glacier (BOJANOWSKI 2010). However, ice accumulation did not last until the next winter season to be considered an actual glacier but according to BOJANOWSKI 2010 the remainders of this experiment after all lasted until 07 May A major downside to this project, especially on a larger scale, is the significant consumption of electric energy to run the lawn sprinklers. Similar activities were performed on Mount Aragats, Armenia. In this experiment water was sprayed from a steel tower. Problems with electric energy do not apply to this project since the pressure in the pipes for the water spray is created by the difference in altitude from the water source a dammed lake and the artificial glacier. The creation of the glacier was started at the beginning of November 2010 and reached heights of up to 6 m as Fig. 3.8 shows. A snow cover contributed to the protection of the artificial glacier. To additionally protect the ice accumulation from melting Geotextile coverage was applied at the beginning of June and thus provided a survival till the beginning of August. Since environmental conditions for each experiment are largely different the experiment s efficiency cannot be compared to one another. 16

31 3 Melt reduction and frozen water storage Fig. 3.8 Artificial glacier created on Mount Aragats in 2010 (photo: A. Hambarian) 17

32 4 Study site 4 Study site 4.1 Introduction to the area of investigation Armenia is a country that is defined by its ancient Christian traditions, the volcanic mountains of the lesser Caucasus range, legacies of the Soviet era and hospitable and imaginative people. It is in a landlocked position surrounded by Turkey to the west, Azerbaijan to the east, Georgia to the North and Iran to the south (Fig. 4.2). Its climate is characterized by a strong degree of continentality with hot arid summers and cold winters with high amounts of precipitation. Fig. 4.1 shows a climate diagram from Armenia s capital Erivan (ca m a.s.l.) by MUEHR 2007 representing most of the country s climate. MUEHR 2007 classified it after KOEPPEN 1918 as BSk a cold semi-arid climate. Fig. 4.1 Climate diagram from Erivan (Eriwan); Data: National Climate Data Center (USA); monthly mean values calculated over the period between ; adopted from MUEHR 2007 On Armenia s highest peak Mount Aragats, only 40 km linear distance from the capital, climatic conditions are vastly different. At an elevation between 3200 m and the mountain s highest point at 4100 m (a.s.l.) low temperatures, strong winds and big amounts of snow dominate the winter months. At 3200 m a.s.l. temperatures of down to -23 C were measured in mid-december As the winds blow from a prevailing NW direction snow masses are redistributed and deposited as regular snow cornices on the downwind side of sharp edges in topography such as those of canyons or mountain ridges (Fig. 4.3). For reasons of accessibility and in order to have an accommodation the snow cornices were investigated in the direct vicinity of a physical research complex the Cosmic Ray Station Aragats, whose task it is to study extraterrestrial radiation. The map in Fig. 4.4 shows its situation at 3200 m a.s.l. next to the dammed Lake Vishab surrounded by snow covered areas. From the countless number of snow covered areas four sites which exhibited a clear aeolian formation were selected to be studied in the field. A compilation of these sites is shown in the map of Fig All of these sites are in different topographic situations: Study site Level 01 Selected snow field Level 02 (Shampoor) Vishab Weather station Topographic characteristics starting in a small cirque continuing along the edges of a canyon part of Level 01, small cirque along the edges of a crest in a small cirque In a small terrain depression 18

33 Fig. 4.2 Geographical situation of Armenia 4 Study site 19

34 4 Study site Fig. 4.3 Snow cornice along the Shampoor ridge on 04 July 2011 (photo: A. Nestler) Fig. 4.4 Overview of the area of investigation. Snow cornices classified from LANDSAT image on 11 June

35 4 Study site Fig. 4.5 Studied snow cornices in the vicinity of Lake Vishab 21

36 4 Study site 4.2 Placement of Geotextiles and debris Several Experiments to relent snow melt were performed on the Vishab snow cornice in 2012 and In m 2 of Geotextiles were successively placed in June and July. In 2013 an area of around 350 m 2 was covered in Geotextiles. The snow under the textiles was partly brought from other sites and compacted afterwards. In part the coverage consisted of a double layer of textiles. In addition around 20 m 2 were covered 20 cm thick layer of debris. The maps in Fig. 4.6 summarize the location of the protective measures in the respective years. Fig. 4.6 a Location of melt-reducing measures in 2012 Fig. 4.6 b Location of melt-reducing measures in

37 4 Study site 4.3 Particularities of snow cornices on Mount Aragats Even as summer advances and most of the general snow cover disappear the cornices still hold significant amounts of snow as Fig. 4.7 illustrates. Another peculiarity that can be observed in Fig. 4.7 is the way the snow is deposited. It is carried over the edge until a certain critical distance is reached and the snow masses get cropped out by sharp cliffs. Fig. 4.7 Snow covered areas along Shampoor ridge on 20 June 2013 (photo. A. Nestler) These cliffs represent the points of biggest snow thicknesses. By means of snow depth sounding maximum snow thicknesses of 5 (Fig. 4.8) to 10 m were observed. Fig. 4.8 Snow cliff. Maximum snow thicknesses of 5 m (photo: A. Nestler) 23

38 4 Study site The snow cornices were observed to appear every year in recurring pattern as the comparison of two images taken over two different years (2011 and 2013) in Fig. 4.9 shows. Their resemblance indicates a similarity of climatic conditions during their formation. a) b) Fig. 4.9 Recurring patterns of snow accumulation. Comparison between a) 06 July2011 and b) 23 June 2013 (photos: A. Nestler) Every event of snow drift adds another layer to the snow cornice. The top of these layers can often be recognized since impurities, such as dust or soot particles, lead to a darker tinge of the snow surface (Fig a). The boundaries of these layers represent break lines along which snow masses topple over or slump down the slopes as Fig b shows. They thus contribute to the loss of snow mass. Fig. 4.10a Winter layers marked by dark snow contaminations (photo: A. Nestler) Fig. 4.10b Rupture of snow accumulations (photo: A. Nestler) Nevertheless, the most important process that contributes to the loss of snow mass is obviously snow melt, which is mainly governed by the surface s geographical position. But also other factors can be observed on Mount Aragats. Especially the heating of volcanic boulders or bed rock through the sun my cause the adjacent snow accumulations to melt as Fig illustrates. 24

39 4 Study site Fig Melt corridors. heating of volcanic rock causes the adjacent snow to melt (photo. A. Nestler) The snow masses that last till mid-july shorten the period for vegetation underneath considerably. The lack of vegetation together with heavily water logged soils beneath the snow cornices lead to a soil creep. Formed by this soils creep terraces (Fig. 4.12) reduce slope angles and thus keep the snow masses in places. The increase in snow accumulation in those positions then leads to a further decrease in the vegetation period and more water percolating into the soils. Hence, this process represents a self-amplifying feedback mechanism. Fig Snow-cornice terraces. Soil creep triggered by a lack of vegetation and melt 25

40 4 Study site water from snow cornices (photo: S. Mohr) 4.4 Observations of melt beneath Geotextile coverage Fig (a-c) illustrate the continuous decay of textile on the Vishab site. Fig decay of Geotextile covered areas on: a) (photo: M. Huss) b) (photo: A. Hambarian) c) (photo: A. Hambarian) In mid-june 2013 abundant snow was available both on covered and uncovered areas. Within one month snow on uncovered areas has already disappeared completely, wheras Covered snow accumulations still hold remarkable amounts of snow. Even after a further month of melt covered snow accumulations still persist. Especially remarkable are the still significant amounts of snow in the central part where snow was artificially accumulated compacted and where melt rates were drastically reduced by the placement of a double-layer textile. 26

41 4.4 Geology and Geomorphology 4 Study site According to NEILL et al. 2013, from a geological point of view Armenia is part of the Turkish Iranian-Plateau (Fig. 4.13) whose existence is owed to the collision between the Arabian and the Eurasian slab and the subduction of the Tethys oceanic slab during Cenozoic times. Fig The Turkish-Iranian plateau and Pliocene- Quaternary centers of volcanism (adopted from NEIL et al. 2012) Initial volcanism was dated to late Miocene (ca. 12 Ma BP (COHEN et al. 2012)) and present volcanism can still be observed in the eastern parts of Turkey, Armenia and Iran (NEILL et al. 2012). The most intensive volcanic events, however, took place in the Pliocene-Quaternary transition around 2.5 Ma BP (COHEN et al. 2012) and its rocks cover the parts of the Armenian administrative provinces of Shirak, Kotayk, Gegharkunik, Syunik and also Aragatsotn (NEILL et al. 2012). In the center of Aragatsotn Mount Aragats is regarded as the biggest center of neo-volcanism during Quaternary times by CHERNYSHEV et al This stratovolcano has an asymmetric shape with a diameter km (CHERNYSHEV et al. 2006). Its main crater is located in its NE section towered over by its main peak, which is the highest elevation within the lesser Caucasus. According to CHERNYSHEV et al Aragats is a polygenic volcano in whose formation three distinct stages of volcanic activity can be distinguished by means of K-Ar dating. The following dates were taken from CHERNYSHEV et al During the first and most productive stages around 900 ka BP eruptions from the main crater and some minor activities in some lateral volcanic centers led to the formation of the main edifice of Mount Aragats. The second stage around 700 ka BP as well as the third stage around 500 ka BP were less intensive according to CHERNYSHEV et al Regular phases of quiescence (about 200 ka) separated each of the stages (CHERNYSHEV et al. 2006). Related to the volcanic activity on Mount Aragatz and fluvial erosion a countless number of canyons and ridges dominate the landscape in a radial pattern around the central crater (Fig.4.17). As shown in Fig areas of large volcanic boulders can be found on the otherwise grass-covered surface. MARUASHVILI et al mentions a glaciation on Mount Aragatz during Pleistocene times and indeed some traces of lateral moraines are discernible in the central crater (Fig. 4.14). At higher altitudes above 3200 m a.s.l. rock glaciers (Fig. 4.16) active and non-active - and polygon soils (Fig. 4.15) were observed as indicators of permafrost. According to DAVOYAN 1971 no clear evidence for recent glaciers exists on Mount Aragats. Instead, a great number of firn patches are present in the upper parts of the mountain. Some 27

42 4 Study site of these pirn patches seem to evolve from snow cornices such as the example in Fig This firn field was observed on 15 June 2013 below the southern peak. Ice occurs at the bottom beneath old snow and indicates permanent existence of snow turning into firn and eventually even to ice. Fig Lateral moraines in the central crater of Aragats (photo: M. Huss) Fig Polygon soils as an indicator of permafrost discernible by slight changes in vegetation cover (photo: A. Nestler) Fig Rock glacier in the central crater linked to a relict later moraine (photo: A.Nestler) Fig Volcanic Boulders in a grasscovered landscape (photo: A. Nestler) Fig System of Canyons around the central crater of Aragats. Hill shade model based on a ASTER DEM 28

43 Fig Firn field evolved from a snow cornice below the southern peak on 15 September 2013 (photo: A. Gerber) 29

44 5 Data 5 Data 5.1 Field Methods All field measurements were carried out during three stays on Mount Aragatz from 2011 to These measurements included GPS mappings, snow depth soundings, ablation measurements and measurements of the snow density. While GPS outline mappings were carried out on all sites introduced in Fig. 4.5 glaciological investigation were only performed on the selected site of Level 01 and on the Vishab cornice. Glaciological investigations included the measurement of snow density, ablation measurements and snow depth soundings. Table 5.1 gives an exact overview of the measurements that were carried out between 2011 and The locations of all glaciological measurements are summarized in Fig Tab. 5.1 Overview of Measurements performed within the area of investigation Site Measurements Date Level 01 Perimeter mapping Level 01 selected Perimeter mapping Snow soundings Snow density Ablation Level 02 Perimeter mappings Vishab Perimeter mappings Snow soundings Ablation Weather station Perimeter mapping

45 Vishab Level 01 5 Data Fig. 5.1 Location of glaciological measurements and protective measures on Level 01 and Vishab in 2012 and

46 5.1.1 GPS devices 5 Data All positions within the area of investigation were determined using hand held GPS devices. Both in 2011 and 2012 the GPS device TRIMBLE GeoXH 2005 Series was used. During the measurements the device operated with an average vertical precision of +/- 9.5 m and a horizontal precision of +/- 5.9 m. Because of a lesser weight the GARMIN etrex 10 was used in Its average precision was not recorded but was observed to be in the range of the TRIMBLE device. Nevertheless, an integrated averaging procedure summarized several GPS points. The accuracy was thus claimed to be improved to +/- 1 m. As this procedure takes around 1 min to process it was feasible for a few points only GPS outline mappings The central element for all measurements was the mapping of snow cornices in the vicinity of the Cosmic Ray station using a hand held GPS-device. In 2011 and 2012 the TRIMBLE GeoXH 2005 Series was used. In 2013 all data was localized using the GARMIN summit e- trex 10. A compilation of the results is shown in Fig The mapping was done by walking along the perimeter of snow covered areas (Fig. 5.2). In order to capture the spatio-dynamic evolution of snow covered area mappings were repeated every year and on several occasions during the field visits. Fig. 5.2 Mapping of snow cornices with a hand-held GPS-device (photo: A. Nestler) Snow depth soundings In order to get an idea of the snow depth and thus estimate the snow volume of the cornices the snow pack was pierced with aluminum probes as shown in Fig These poles consisted of a modular system of 1-m long poles that were combined together to reach the desired length. The soundings with those poles were done in a dense grid in order to capture small scale variations. Determining one single snow depth per point was, however, not trivial. Ice layer in the subsurface (Fig. 5.4) and an underground that consisted of volcanic boulders made a clear sounding result difficult. Two to three soundings within a radius of about 2 m were averaged out in order to minimize these measurement uncertainties. Furthermore the aluminum probe is highly flexible which makes it difficult to keep the probe straight while driving it into the snow pack. Every point s location was recorded with a GPS device. 32

47 5 Data Fig. 5.3 Snow depth sounding using an aluminum probe. Locations are recorded with a handheld GPS device (photo: A. Nestler 2012) Fig. 5.4 Ice layers within the snow pack complicating snow depth soundings (photo: A. Nestler 2012) Ablation measurements Ablation measurements are an established method to determine melt rates on glaciers. For this purpose stakes are driven into the surface. Snow or ice melt causes the snow surface to drop. Melt rates can now be determined as the change in the distance between the tip of the stake and the snow surface as Fig. 5.4 shows. The material of those stakes should have a low thermal conductivity in order to prevent a melting of the stake into the snow surface. Hence, wood or plastic are to be preferred. In this study wooden stake with a length of 2 m were used. They were driven into the snow pack down to their tip in order to provide a long measurement period without having to reinstall them in-between. A reinstallation of the stakes would increase measurement uncertainties. With the help of the ablation stakes the melt reducing effect of the Geotextiles could be determined by comparing melt rates on and right next to the textile-covered areas. Unfortunately many of the ablation data series of 2012 are incomplete since many poles were lost due to vandalism. Nevertheless some meaningful data could still be extracted. Regarding both temporal resolution and the total time span of measurements the data series of 2012 and 2013 differ significantly. In 2012 ablation readings were gathered on a highly irregular basis ever 4 to 15 days. By contrast, in 2013 ablation stakes were read every other day and one stake installed next to the Cosmic Ray Station (STA13_01) even 2 to 3 times a day. Readings from the latter were used to illustrate diurnal melt cycles. Melt rates in 2012 were measured over a time span of 37 days between 17 June 2012 and 24 July 2012, while melt rates in 2013 were only measured during 4 consecutive days between 22 and 26 June The further the measurements progressed the more lateral melt occurred around the ablation stakes. The resulting uncertainties are estimated to be in the range of 1 to 2 cm. 33

48 5 Data Fig. 5.4 Ablation measurements on the Vishab snow field in 2013 (photo: M Huss 2013) 34

49 5 Data Snow density measurements In order to draw conclusions concerning the water that is stored in the cornices, snow densities were measured in a snow pit (Fig. 5.5). The location of this snow pit was chosen on a site of average snow depth, which was observed to be about 3 m. In 2012 measurements reached down to a depth of 183 cm and in 2013 snow density was measured down to a depth of 273 cm. Readings of density were obtained by weighing the contents of a cylinder with a defined volume that was driven into the snow pack (Fig. 5.6). The cylinder that was used here was produced by the Laboratory of Hydraulics, Hydrology and Glaciology (VAW) of the ETH-Zürich. It has a height of 55 cm and a cross sectional area of 70 cm 2 (PLATTNER 2004). The weight of the cylinder was determined with a PESOLA spring balance. It measures both weight in g and from the weight deduces the equivalent of water that would correspond to the snow contained in the cylinder. This equivalent is termed snow water equivalent SWE in mm water equivalent (w.e.). In order to calculate snow density in in g cm -3 form the SWE in mm and the snow height SH in cm the following formula as presented in PLATTNER 2004 is applied: (5.1) Fig. 5.5 Snow pit dug to obtain snow density data down to a depth of 3 m (photo: A. Nestler) Fig. 5.6 Cylinder with a defined volume to infer snow density from the weight of snow (photo: A. Nestler) 35

50 5.2 External Data 5 Data Weather data One very important element of this investigation is weather data. By a fortunate coincidence an automated weather station, shown in Fig. 5.7, was installed within the compound of the Cosmic Ray Station. It is run by the Cosmic Ray Division of Yerevan Physics Institute and started operating on 3 June 2011, which coincided with the first field visit to Mount Aragatz. Readings are measured about 7 m above the ground. For its proximity to the area of investigation no significant data extrapolation is necessary. The readings are recorded minutely and are updated regularly on The data series used in this study reaches till 24 June From the multitude of measured parameters the following are of interest for this study: - air temperature [ C] - wind speed [m s -1 ] - maximum wind speed [m s -1 ] - wind direction [ ] - precipitation rates [mm h -1 ] - solar irradiance [W * m -2 ] The parameters are exported in an irregular time format and have to be interpolated for hourly readings. Fig. 5.8 shows a compilation of the most important meteorological parameters averaged for a daily resolution in the period of June 2011 to July Fig. 5.7 Automated weather station at the compound of the Cosmic Ray Station Aragats (Photo: A. Nestler) (photo: M. Huss) 36

51 T [ C] Prec. rate [mm h -1 ] Wind velocity [m s -1 ] Wind direction [ ] solar radiation [W m -2 ] Data Jul 2013 Jun 2013 Mai 2013 Apr 2013 Mrz 2013 Feb 2013 Jan 2013 Dez 2012 Nov 2012 Okt 2012 Sep 2012 Aug 2012 Jul 2012 Jun 2012 Mai 2012 Apr 2012 Mrz 2012 Feb 2012 Jan 2012 Dez 2011 Nov 2011 Okt 2011 Sep 2011 Aug 2011 Jul 2011 Jun

52 5 Data Fig. 5.8 Daily average readings of air temperature, precipitation rate, wind velocity, wind direction and solar radiation. 10-day moving averages are represented by a black trend line. Rain records exclusively involve liquid precipitation. Wind direction readings are filtered for wind speeds above 1 m s Temperature The air temperature in the period between June 2011 and July 2013 amounts 0.3 C on average. The hottest daily mean temperature was recorded on 31 July 2011 with 16.7 C and the coldest on 03 March 2012 with C. These peak temperatures do not seem to represent extraordinary events since recorded daily mean temperatures around these dates are within a similar order of magnitude. Monthly averages point in a similar direction. July and August were found to be the hottest months for all three years. In total August 2012 was the hottest of the whole period with 11.6 C. The months of January, February and March were the coldest months of 2012, where February 2012 was recorded to be the coldest month with C on average. By contrast November and January were the coldest months of 2011 and 2013 respectively Wind direction, wind speed, precipitation Wind direction data cannot simply be averaged (personal communication M. HUSS 2013). If a number of wind directions are averaged the resulting wind direction will point in many cases in an erroneous direction. For instance should the average of 355 and 5 be 0. A simple average, however, would return a value of 180. For this reason all wind direction readings were translated into vectors. Averaging operations were exclusively performed in the vector format and translated back into degree readings afterwards. Further sources of errors are random wind directions (personal communication M. HUSS 2013). If wind speeds are too low it may occur that wind vanes point in any directions and noise is added to the data. By filtering all wind directions which are recorded below a certain wind speed threshold this noise may be reduced. In this case all wind directions recorded below a wind speed of 1 m s -1 were filtered out. In order to identify the dominant wind directions wind directions were counted in intervals of 30. For the whole period of meteorological records from 06 June 2011 to 29 July 2013 Fig. 5.9 illustrates the distribution of wind directions as a share in the total number of counted wind readings. This graph clearly identifies NNW as the most dominant wind direction throughout the whole period. Besides NNW W and NEE are also dominant in the distribution of wind directions. 38

53 5 Data June July % N % W % 0.0% 70 E S 150 Fig. 5.9 Distribution of wind directions on Mount Aragats over a period of 3 years Wind directions in winter time seem to be more homogenously distributed. However, as the comparison between the winter of and the winter of shows the conditions may vary over the years (Fig. 5.10). Winter Winter N 0.8% N 0.8% % % 70 W 0.0% E W 0.0% E S Fig Wind directions in winter and S The comparison of wind directions in summer time reveals a remarkably similar bimodal distribution pattern with NW being the prevailing wind direction but also a clear preference for winds blowing from a SE direction (Fig.5.11). 39

54 Wind direction [ ] Wind velocity [m s -1 ]] Wind direction [ ] Rain rate [mm h -1 ] 5 Data 330 Summer 2011 N 0.8% Summer % N % % W 0.0% E W 0.0% E S Fig Wind directions in summer 2011 and S 150 This pattern can be attributed to a mountain breeze and valley breeze circulation. This hypothesis is supported both by observations in the field and an hourly averaging of readings over the summer months of 2011, 2012 and This diurnal variation can furthermore be linked to wind speed and precipitation patterns as Fig shows :00 6:00 12:00 18:00 Time Wind direction Wind velocity Fig. 5.12a Diurnal average wind directions and wind velocities in August (2011,2012) :00 6:00 12:00 18:00 Time Wind direction Rain rate Fig. 5.12b Diurnal average wind directions and rain rate in August (2011,2012) 0 The comparison of wind direction and wind velocities in August (Fig a) show that strong winds tend to occur from NW, whereas SW winds are much weaker. Especially in summer time precipitation was found to be correlated to certain wind direction. As Fig b shows most precipitation occurs around noon as after math of convection which is expressed by a wind blowing from a NW direction Precipitation data are known to be affected by systematic errors (WAGNER 2009). This systematic error mostly consists in an underestimation due wind keeping the entire precipitation from entering the gauges. In this study a constant correction factor is used adding 50 % to the recorded precipitation. This factor minimizes the error does, however, not yield actual precipitation amounts since after WAGNER 2009 the error is largely dependent on wind speed and most importantly the type of precipitation. Most of all solid precipitation in the form of snow is affected by this systematic error to a degree that almost no precipitation is recorded in winter time. It may, however, occur that snow melts under negative air temperatures due to a heating of the measurement material and results in a record of liquid precipitation. This precipitation date can be recognized as solid by its occurrence below a certain temperature threshold. 40

55 5.3 Other data 5 Data ASTER GDEM All modeling approaches are based on an ASTER global digital elevation model (GDEM). The acronym ASTER stands for Advanced Spaceborne Thermal Emission and Reflection Radiometer. It operates in 14 spectral bands ranging from visible to near infrared and covers the earth surface from 83 N to 83 S (ABRAMS et al. 2008). The ASTER GDEM provides a horizontal resolution of 1 (JACOBSEN 2010) per pixel. This resolution corresponds to roughly 27 m within the area of investigation. Aster Data is publicly accessible on: Topographic map 1: Infrastructural features, rivers and bodies of water were digitized from a topographic map at a scale of 1: which was kindly provided by the Institute of Geological Sciences at the National Academy of Sciences, Yerevan. Since the map was referenced based on a PULKOVO system minor corrections in terms of geometric distortion had to be carried out LANDSAT satellite image With the LANDSAT program NASA has managed to continuously cover the earth s surface with multispectral images. Several generations of satellites have been launched as part of this program since Ever since, the onboard sensors have been improved in terms of spatial resolution and the spectral bandwidth. For this study a seventh generation satellite, the LANDSAT 7, was used. It is equipped with the Enhanced Thematic Mapper Plus (ETM+) sensor which records images in 8 spectral bands ranging from visual near infrared to long wave infrared (IRONS 2013). The 8 th band was found to be most suited for this study as it covers the widest range of wavelengths from green (0.52 µm) to near infrared (0.9) and its high resolution of 15 m per pixels provides detailed information. One LANDSAT scene with the above mentioned specifications showing snow distribution on 11 June 2006 for the entire Aragats area was obtained from: Snow has a very high reflectance of electromagnetic radiation for wavelength within the visible spectrum (O BRIEN et al. 1975). Snow covered areas could thus be easily discerned in the satellite image due to the big spectral difference between snow and bare ground. 41

56 Repeated every hour Repeated each day Different scenarios: different altitudes, textile coverage 6 Methods 6 Methods 6.1 The Glacier Evolution Runoff Model In order to simulate the development of snow accumulations over time the glaciohydrological model GERM (Glacier Evolution Runoff Model) by HUSS et al. 2008b was used. The original version of the model was designed to simulate the evolution of glaciers and runoff from highly glaciated areas. It incorporates four modules as described in HUSS et al. 2008b which build upon one another: 1) the glacier s surface mass balance which accounts for accumulation and ablation, 2) evaporation, 3) a runoff-calculation and 4) the final computation of the change in the glacier s topography and its retreat. This study, however, looks at snow accumulations exclusively. The model had thus to be adjusted accordingly. Figure 6.1 gives an overview of the schematics of GERM adapted to melt simulations of snow accumulations. Scenarios for seasonal changes in temperature and precipitation Time series of hourly T and P readings Model outputs Mass balance Snow volume change Surface type (fresh / old snow) T and P at every grid cell Mass balance model Solid Precipitation Snow Melt Hourly evaporation Evaporation Model Daily runoff water balance at every grid cell Runoff model 3D glacier surface Snow surface updating Fig. 6.1 Schematic overview of GERM; modified after HUSS et al. 2008b The calculation of mass balances within the modified GERM is based on the energy balance approach introduced by OERLEMANS 2001 as well as on the distributed temperature-index approach presented by HOCK The model was run for a three-dimensional representation of the selected snow field, with a resolution of 1x1 m. It uses precipitation and temperature data recorded by a weather station in the direct vicinity (ca. 200 m) of the snow field. In order to observe the impact of geotextilecoverage the model was run both with and without a simulated coverage. To investigate the survival of snow accumulations in different areas on Mount Aragatz the evolution of the snow field was simulated for different elevations. 42

57 6 Methods Snow surface mass balance calculated with the help of the energy balance approach In the energy balance approach GERM calculates cumulative mass balances (B cum ) at the snow surface in meter water equivalent (m w.e.) for each grid cell (1 m resolution) and for every time step (t+1) of the respective day individually in the fashion of MACHGUTH et al as follows: ( ) ( ) [ ( ) ] (6.1) In this formula time in a discrete interval of one hour is represented by the variable t. L is the latent heat energy available for melting. E is the energy flux at the surface averaged over one day. Accumulation is accounted for as solid precipitation (P SOLID ). Solid precipitation is calculated after FARINOTTI 2012 as: ( ) (6.2) Solid precipitation is defined as any precipitation below a temperature threshold which in this case was set to 1.5 C. In order to account for a distinction between snow and rain, r s is introduced as a Boolean variable assuming a value of 0 for temperatures above the threshold and 1 for temperatures below the threshold. is precipitation either measured or interpolated to a reference location, which is preferably in the middle of the investigated snow field. The factor is introduced in order to account for gauge under catch errors which occur due to the influence of wind. The accumulation of snow is not homogenously distributed, but, after HUSS et al. 2008b, rather affected by wind-drift and avalanches. Indicators for these processes could be derived from the DEM. Avalanches were disregarded for this snow field, since steep slope inclinations of 40 to 60 that would be suited for avalanche-action (HUSS et al. 2008a) could not be found in the direct vicinity of the snow field. As opposed to this, the effect of wind action plays a role in every topographic position. By taking curvature as an indicator snow erosion and redistributions were considered in the model and are expressed by the parameter. It is assumed that all melt water is immediately and completely evacuated from the snow accumulation. Hence latent heat fluxes generated from refreezing are not considered here. This means that energy responsible for melt processes (E) can be calculated according to OERLEMANS 2001 as: ( ) (6.3) where is the sum of the long-wave radiation balance and turbulent heat fluxes. As T is given in C the balance is linearized around the freezing point, i.e. if T is O C C 1 is 0, too. As suggested by OERLEMANS 2001 C 1 is kept at 10 W m -2 K -1 as a constant. C 1 can be freely adjusted and is used for the calibration of the model. I pot describes the potential extraterrestrial (solar) short-wave irradiance. I pot does, however, not include the reduction of radiation intensity by clouds and aerosols as solar radiation passes through the atmosphere. The factor d is a constant factor accounting for these effects. In this case a constant factor of 0.7 is used. α is the snow surface albedo. Two different values for albedo are used in order to distinguish between old and fresh snow. The whole term ( ) describes the effective solar radiation arriving at the snow surface. 43

58 6 Methods Snow surface mass balance calculated with the distributed temperature-index approach In this approach GERM calculates ablation for every grid-cell of the DEM (1 m resolution) at hourly time steps. In contrast to the energy-balance approach the temperature-index approach puts more emphasis on hourly mean air temperatures. The term distributed refers to temporal and spatial variations of melt processes caused by varying radiation fluxes. Hence melt rates M in mm d -1 are calculated as suggested by HOCK 1999 as: { ( ) (6.4) where MF is a factor describing melt in mm d -1 C -1. As indicated by its unit this factor is calculated on a daily basis. In order to scale it for hourly time steps it is divided by the number of time steps per day n = 24. r fsnow is a radiation coefficient for fresh snow and r osnow refers to old snow. I pot describes the potential clear-sky direct irradiance from the sun (W m -2 ). T is the air temperature in C. In this approach melt is assumed to be 0 for temperatures below 0 C Snow surface albedo In this model two different albedos for old and fresh snow are defined. During the measuring period the albedo for old snow is assigned to the whole snow cornice area. In the case of a snow fall event albedo values of fresh snow are assumed. However, the latter rarely occurs during the summer season Subroutine: Potential extraterrestrial (solar) short-wave radiation The potential extraterrestrial shortwave radiation I pot in both approaches is not treated as a constant. Instead it is calculated in a subroutine of GERM after HOCK 1999 as a function of solar radiation at the top of the atmosphere, geometric features of topography, the transmissivity of the atmosphere and the earth s trajectory relative to the sun in the following way: ( ) ( ) (6.5) The solar constant is expressed by I 0. As suggested by FROEHLICH 1993 a value of 1368 W m -2 is assumed here. R m R -2 includes the earth s orbit. It serves as a correction factor for the earth s eccentricity, where R represents the earth-sun distance at the time of the calculation and R m the average earth-sun distance. Filtering effects of the atmosphere under clear-sky conditions are accounted for with the transmissivity factor. is a variable factor (HOCK 1999). As no means for measurement is available in this study, this factor is kept constant at 0.75 (HOCK 1999) and thus within the range that was determined in a study by OKE Moreover, HOCK 1999 claims this factor to be sufficiently accurate as does not have any direct impact on the calculation of melt energy. It is rather used as an index scaled by the empirical coefficient a. P is the mean air pressure at the altitude of the calculation. P 0 is the barometric pressure at sea level. P/P 0 thus describes the influence altitude has on radiation, 44

59 6 Methods i.e. higher direct solar radiation at higher altitudes (HOCK 1999). Z represents the zenith angle. Θ expresses the angle at which direct solar radiation impinges on the snow surface. In order to calculate the angle of incidence HOCK 1999 uses an algorithm proposed by GARNIER and OHMURA 1968: ( ) (6.6) where β is the slope angle in question. φ sun is the azimuth of the sun and φ slope is the aspect of the respective slope. The zenith angle Z is calculated after IQBAL 1983 as where φ is the geographical latitude, the sun s declination and the hour angle. Potential direct solar irradiance goes down to 0 after sunset and remains at 0 until sunrise (HOCK 1999). As zenith angles at sunrise and sunset respectively are at π/2 [radians] (90 ) the hour angles for sunrise Ω and sunset Ω are calculated as proposed by IQBAL 1983 as In order to obtain the sun s declination (in radians) on any date of the year (YD [1,365]) an empirical formula by IQBAL 1983 is used: (6.7) (6.8) ( ) (6.9) Direct solar irradiance is also set at 0 if a grid cell is shaded from the sun by the surrounding topography. After OERLEMANS 2001 the shading angle ɸ is defined by the surrounding skyline and varies with the sun s azimuth and elevation above the horizon. This procedure is derived from the GDEM. Potential direct radiation from the sun is calculated in time steps of 10 min and is averaged out at 1 h intervals Evaporation GERM calculates the potential evapotranspiration on an empirical basis as suggested by HAMON As described in HUSS et al. 2008b the approach by HAMON 1961 was enhanced and adjusted for an application in the catchments of high mountains by considering distinct surface types, interception and actual evaporation. The potential evaporation E pot in mm d-1 is calculated after HUSS et al. 2008b on the basis of air temperature T and the saturation vapor pressure e S for every grid cell with an hourly resolution as (6.10) In this equation DL is the fraction of daylight per day, which is an expression of the day of the year. S type is an empirical factor used to distinguish the influence different types of surface have on evaporation. The surface types distinguished in this model are debris and snow. For both types a value of 1.25 mm d -1 was assumed. E pot remains at a potential level as long as sufficient water is provided by an interception reservoir. The interception reservoir is charged during rain fall events and is a superficial humidity that does not infiltrate. Its volume is largely dependent on the surface type (FARINOTTI et al. 2012). In this case a size of the interception reservoir of 1 mm was assumed for debris covered areas. On snow covered areas no interception occurs (personal communication M. HUSS 2013). 45

60 6 Methods Furthermore, evaporation on snow covered areas varies throughout the year being highest in winter time (HUSS et al. 2008b). Once the interception reservoir is used up the potential evaporation is turned into actual evaporation using a constant reduction factor which depends on the surface type. Both for debris and for snow a reduction factor of 1.25 mm d -1 is assumed. On snow covered areas evaporation is always at a potential level during melt times. The water that is thus lost is subtracted from the melt water (snow water equivalent) at time steps of one hour. On grid cells that do not contain snow the amount of evaporated water is subtracted from the interception reservoir or from the amount of water that is stored in the fast reservoir once the interception reservoir is empty. If the fast reservoir is not sufficiently filled the differential amount is subtracted from the slow reservoir (FARINOTTI et al. 2012). In case of a complete depletion of the slow reservoir evaporation is reduced to the amount that is maximally available (FARINOTTI et al. 2012). The concept of reservoirs is described in the following chapter runoff routing. As this investigation requires an hourly resolution GERM was adapted in a way that all values are divided by 24. Sublimation is also implemented in GERM. However, it is rather significant under conditions in tropical high-mountain regions and not on Mount Aragats Runoff Routing Just as in glacierized catchments (JANSSON et al. 2003) highest runoffs on Mount Aragatz are induced by snow melt during the hottest and driest periods of the year. When it comes to calculating the runoff most melt-models (e.g. SCHAEFLI et al. 2005) clearly distinguish between ice-covered areas, such as glaciers or isolated ice patches, and ice-free areas. As neither glaciers nor ice patches could be found on Mount Aragatz all runoff is calculated for ice-free areas. The amount of water that is available for runoff Q is obtained as described by FARINOTTI et al by calculating the local water balance at each grid cell: (6.11) where P liq is all liquid precipitation and M is the snow melt, is the change in the reservoir (r) volume. The routing model works according to the principle of linear reservoir proposed by BAKER et al In a linear reservoir model the runoff Q from a reservoir r is proportional to the volume V r of water it contains. The time it takes to empty the reservoir is expressed by the retention constant k r. This relationship can be expressed after HUSS et al as: (6.12) Outside snow-covered areas runoff water in the model is assumed to be routed through two different parallel reservoirs: a slow one through an underground and soil storage and a fast one with a direct runoff on the surface (SCHAEFLI et al. 2005). The interception reservoir does not contribute to runoff (FARINOTTI et al. 2012). Both reservoirs can be expressed by two different retention constants: k slow and k fast. 46

61 6 Methods Both melt water and rain water are divided into one part infiltrating into the ground and thus contributing to the slow storage V slow and one part running off directly contributing to the fast storage V fast. The volume that is added to the slow reservoir is inversely proportional to its filling level. It is calculated after SCHAEFLI et al as: ( ) ( ( ) ) (6.13) Snow melt M and liquid precipitation P liq are both given in mm d -1. V slow_max is the maximum capacity of the reservoir (in mm). The exponent f v is a factor to be tuned. It is set to c = 2 (SCHAEFLI et al. 2005). As described in HUSS et al k fast at each grid cell may assume two distinct values according to the surface type Snow surface updating In contrast to the original GERM this model routine is performed in time steps of one day. At each snow covered cell of the grid melt in mm w.e. occurring in the course of one day is subtracted from the total snow depth until snow depth is 0. This is when the model assigns the property of bare ground to the cell. All melt rates are scaled with the help of the snow density measurements. If the model calculates melt in mm w.e. it is translated into mm surface decrease as a function of snow density Extrapolation of results In order to simulate coverage with geotextiles the model was run with a constant melt reduction factor y, calculating melt beneath coverage M c as: (6.14) By assuming a constant temperature and precipitation lapse rate with altitude it is possible to extrapolate modeling results to different altitudes on Mount Aragatz. The model uses a gradient for precipitation of +2 % / 100 m and temperature of -0.6 K / 100 m. 47

62 7 Calibration 7 Calibration The models that are presented in this study use meteorological readings to deduce melt processes on a snow surface. These readings, however, only capture a small part of the entire complex system and in fact, measuring all the processes involved is not possible. The following two examples will demonstrate these uncertainties: The calculation of solar irradiance only accounts for standard cloud-free conditions which are in reality rarely the case. Other uncertainties in terms of solar irradiance are slight variations in surface topography which may have a significant impact on the solar energy that impinges on a surface. A DEM with a resolution of around 30 m can of cause not capture all these minor variations. In order to compensate for these uncertainties parameters are used to match model outputs to the environmental conditions at the respective area of investigation. Hence the goal of a calibration is to match measured readings to those calculated by the model. In this case melt rates were measured in various locations on the snow field and at various points in time. Since there are no significant differences between the different measurements the longest time series of measurements was picked for calibration. Both models were now run various times, each time with a different combination of parameters. An iterative approximation of calculated to measured readings was thus possible. 48

63 7 Calibration 7.1 Calibration of the distributed temperature index model Three parameters in the model are available to be adjusted in order to calibrate the model. These parameters are: the melt factor MF, the radiation coefficient for fresh snow r fsnow and the radiation coefficient for old snow r osnow. These parameters can however not be adjusted freely without restrictions. Both radiation coefficients are a direct representation of the surface albedo and thus need to be kept within reasonable bounds. The proportion of old-snow to fresh-snow albedo was applied to both coefficients which is constant at: 1 : 1.6. One of the main purposes of this modeling is an extrapolation of results beyond the measuring period. For this reason first priority of the calibration was to match simulated and measured ablation readings for the last point in time when ablation was measured. All intermediate points of measured and calculated readings were then tried to be approximated as good as possible. Ablation measurements being relevant for calibration were performed at the following points in time: 0 21 June June July July 2012 The meteorological data contained both data series of rain and rain rate. After a detailed scrutiny the rain data series turned out to contain implausible readings and did not match those of the rain rate series. Before the discovery of this error a first calibration was done with the corrupt rain data. The model output was thus useless. Nevertheless, the calibration procedure taught an important lesson on the impact of the different parameters on the model output. The graph in Fig. 7.1 shows the deviation of simulated ablation from measured ablation in the various model experiments during calibration at the 4 points in time. Fig. 7.1 is accompanied by Tab. 7.1 listing the parameter configurations for each modeling experiment. 49

64 Deviation from measured ablation [cm] Deviation from measured ablation [cm] 7 Calibration Point in time Measured Experiment 01 Experiment 02 Experiment 03 Experiment 04 Experiment 05 Experiment 06 Fig. 7.1 Calibration I of the Degree-Day Model with wrong precipitation data. Deviation of measured melt from calculated melt. Demonstrates the impact of parameters on model behavior Tab. 7.1 Parameter configurations for calibration I with wrong precipitation data Exp. 01 Exp. 02 Exp. 03 Exp. 04 Exp. 05 Exp. 06 MF r snow r ice During calibration MF turned out to have significant importance for the temporal variability of melt. Deviations from measured readings show a clear dependence of the factor MF. An increase in MF increases deviations and vice versa. So the closest match with measured readings can be obtained by minimizing MF and adapting r snow and r ice in a way that modeled and measured melt coincide in point 4. With the knowledge gained from calibration I a suited parameter configuration was quickly found with: MF = 1.24, r snow = 0.58 and r ice = The deviation of modeled melt from measured melt is shown in Fig Original Experiment01 Experiment Point in time 50

65 7 Calibration Fig. 7.2 Calibration II of the Degree-Day Model with correct precipitation data. Deviation of modeled melt from measured melt With these calibration parameters modeled melt rates do not deviate more than 7 cm from measured melt rates. 7.2 Calibration of the energy balance model The calibration of the Energy-Balance Model is possible with the help of the following parameters: C 0 and C 1 expressing the surface s long-wave radiation budget and two distinct readings for the surface albedo of fresh snow α snow and for old snow α ice. As with the Degree-Day Model not all parameter can be altered without restrictions. Albedo values are varied within the ranges recommended by CUFEY and PATERSON These ranges are: 0.75 to 0.98 for fresh (dry) snow and 0.50 to 0.60 for old (debris-rich) snow. Moreover C 0 is suggested to be kept constant (MACHGUTH et al. 2006, OERLEMANS 2001). This leaves C 1 as the actual tuning parameter for the calibration. As in the Degree-Day Model C 1 was adapted in a way that melt rates matched at the last point in time when melt rates were measured. One parameter constellation was found that resulted in the closest match possible with a deviation of less than 10 cm. This parameter constellation consists of: C 0 = 55.00, C 1 = 10.00, α snow = 0.90 and α ice = The deviations of modeled form measure melt readings at the four points in time are shown in Fig

66 Deviation from measured ablation [cm] 7 Calibration Measured Experiment Point in time Fig. 7.3 Calibration of the energy balance model 52

67 8 Validation 8 Validation 8.1 Validation of snow depth soundings As described in chapter 5 snow depth soundings were subject to particular difficulties. These consist in icy layers which make an identification of the underground difficult, a highly uneven underground and the flexible material of the probe which made it difficult to pierce the snow pack in a straight line. From these uncertainties the question of reliability of the measurements arises. Keeping in mind that snow accumulation show annually repeating patterns a comparison of soundings of the same place over two distinct years is a way to validate their quality. Fig. 8.1a and b shows interpolations of snow depth soundings performed in 2012 and Fig. 8.1a Interpolated snow depth soundings on 17 June 2012 Fig. 8.1b Interpolated snow depth soundings on 22 and 23 June Both interpolations show a good agreement of the snow depths distribution over the two years. Similar snow depth distribution patterns were captured with the soundings of both years. The total snow depths, however, are different over the years. Despite the larger areal extent of snow covered area in 2012 snow depth are lower than in This could be explained by differences in distribution patterns but could also indicate incorrect snow depth soundings. Besides the purely optical comparison a correlation of snow depths over both years could give a further hint on the reliability of the sounding data. For this purpose interpolated snow depths of both years were sampled at each grid point of the interpolation of 2012 and compared as shown in Fig The correlation coefficient resulting from the comparison is The deviation from a perfect correlation is of cause explained by differences in snow distribution but also by differences in sounding performances. 53

68 8 Validation Interpolated snow depth 2013 [cm] As the correlation is quite high one may assume an overall good performance of the snow depth soundings. 500 Fig. 8.2 Interpolated snow depth of 2012 and 2013 in comparison Interpolated snow depth 2012 [cm]

69 8 Validation 8.2 Validation of the performance of GERM As presented in chapter 5 the perimeter of snow covered area was repeatedly mapped during the same year. The comparison of this data with perimeters calculated by the model gives a feedback on the performance of the model. Fig. 8.3a and 8.3b opposes both calculated and mapped perimeters for both the energy balance and the distributed temperature index model at 3 different points in time. Fig. 8.3a Comparison of perimeters simulated by the temperature-index model and mapped in the field Fig. 8.3b Comparison of perimeters simulated by the energy-balance model and mapped in the field On 20 June both simulated and mapped outlines coincide at the initiation of the model. After 18 and 33 days respectively an overall good agreement between modeled and mapped outlines can still be observed for both models. However, small scale variations are discernible in the morphology of mapped perimeters that are not reproduced by each of the models. These small scale variations in melt are an effect of radiation varying with topography. The low-resolution GDEM used in this study just does not include the corresponding small scale topographic variations. Furthermore inaccuracies of snow depth soundings and the interpolation between measured points are other influences contributing to the inaccuracy of the simulation. Comparing the performance of both models shows a slightly better fit of the energy balance model. The temperature index model seems to overestimate melt especially at intermediate points in time (08 July 2013). 55

70 cummulative snow melt [cm snow height] cumulative snow melt [cm snow height] Results 9 Results 9.1 Ablation measurements Ablation measurements on snow surfaces carried out in 2012 show similar ablation rates for the different topographic positions as Fig. 9.1 shows. Date LE112_01_Snow LE112_02_Snow LE112_03_Snow LE112_04_Snow LE112_05_Snow LE112_06_Snow VIS12 01_Textile VIS12 02_Snow Fig. 9.1 Cumulative snow melt in June and July 2012 Date VIS13 01 _ Snow VIS13 02 _ Textil VIS13 03 _ Textil VIS13 04 _ Snow VIS13 06 _ Snow LE _ Snow LE _ Snow LE _ Snow Fig. 9.2 Cumulative snow melt in June

71 Average daily snow melt [cm/d] Snow, Level 01, 2012 Snow, VIshab, 2012 Snow, Level 01, 2013 Snow, VIshab, 2013 Textil,single layer, 2012 Textil,single layer, 2013 Textil, double layer, Results Furthermore, the longest times series (LE112_05) suggests a rather continuous melt process. Additional ablation measurements on the Geotextile placed in 2012 (VIS12_01) show a significant reduction of melt rates by 57 % (Fig. 9.2) compared to measurements performed right next to the textile covered area. With a similar set up a melt reduction of 55 % was measured in 2013 underneath slightly compacted snow. These melt reduction factors clearly lie within the range found by OLEFS and FISCHER 2007a. Coverage with a double layer was found to reduce melt rates by 75 %. Regarding average daily ablation readings from 2012 and 2013 both lie within the same order of magnitude. While melt rates on bare snow surfaces in 2012 were lower than in 2013, melt rates beneath textile coverage were lower in 2013 compared to For bare snow melt rates in both years were furthermore observed to be constantly lower on the Level 01 cornice compared to the Vishab cornice. In 2012 melt rates on Level 01 amounted to -7.8 cm d -1 on average, while on the Vishab cornice average melt rates of -8.4 cm d -1 were recorded. In 2013 a similar trend was observed with -8.9 cm d -1 on Level 01 and cm d.1 on Vishab. For single layer coverage with Geotextiles on Vishab melt rates of -5.0 cm d -1 were recorded in In 2013 a similar set up resulted in melt rates of -4.3 cm d -1 and a double layer set up even lowered melt rates down to -2.5 cm d -1. Fig. 9.3 summarizes daily average melt rates Fig. 9.3 Average daily melt rates in June and July. Minima and maxima are indicated with error bars The ablation stake STA13 01 was read several times during the day and provides data with a high temporal resolution. Different phases of melt intensity in the course of a day could thus be distinguished. Fig. 9.4 shows cumulative snow melt in the course of 4 days. 57

72 Cumulative snow melt [cm snow height] 10:00 22:00 10:00 22:00 10:00 22:00 10:00 22:00 10:00 9 Results Day time from till STA13 01 _Snow Fig. 9.4 Cumulative snow melt at stake STA13 01 between 22 June and 26 June Gray bars indicate night time Over the entire period of 4 days 41 cm of snow melt were recorded at stake STA13 01, whereby melt occurred almost exclusively during day time as Fig. 9.4 clearly shows. Within this period the average ablation rate during day time amounts to 0.81 cm h

73 Depth layers beneath the surface [cm] Depth layers beneath the surface [cm] 9 Results 9.2 Snow Density Fig. 9.5 summarizes snow density measurements performed in 2012 and Fig. 9.6 those performed in A comparison of both charts shows that snow densities in both years lie within the same range. However, the average density in 2012 was with 0.59 g cm -3 lower than the average density measured in 2013 which was 0.63 g cm -3. Further differences can be observed in the vertical distribution of densities. While the distribution of densities with depth appears to be rather homogenous in 2012, it shows significant jumps in Notably prominent is an increase in densities within the top layers between 0 and 49 cm beneath the surface. Within those layers density increases to 0.66 g cm -3. The bottom layer between 242 and 273 cm beneath the surface shows a similar increase in densities to 0.65 g cm -3. Snow density in remaining layers stays beneath 0.62 g cm -3. ρ [g/ cm^3] Fig. 9.5 Snow densities in ,5 ρ [g/ cm^3] Fig. 9.6 Snow densities in

74 9.3 Perimeter mappings 9 Results Inter-annual comparison Fig. 9.7 presents a comparison of perimeters of the Level 01 snow cornice mapped in three consecutive years. Fig. 9.7 Comparison of perimeters of Level 01 mapped in 2011, 2012 and 2013 The comparison reveals annually recurring patterns, but also distinct features, such as tongues reaching down from the main body of snow. Similar observations can be made by comparing Perimeters of the Level 02 cornice in Fig A comparison of the perimeters of the Vishab cornice over two years (Fig. 9.9) shows a big similarity in the central part. In 2013 a large area covered by snow stretches out in the northern section of the snow cornice which is not present in

75 9 Results Fig. 9.8 Comparison of perimeters of Level 02 mapped in 2011, 2012 and 2013 Fig. 9.9 Comparison of perimeters of Vishab mapped in 2012 and

76 62 9 Results

77 9.3.2 Intra-annual comparison 9 Results A comparison of perimeters on several dates in one year is shown in Fig This comparison documents the successive retreat of snow covered area and illustrate the effect of Geotextiles. Fig Perimeter of snow covered area at the Vishab site on several dates during the year 2012 Comparing extensions of snow fields on larger scales such as represented in Fig reveals final retreat areas for snow towards the end of the season. 63

78 9 Results Fig Perimeter of snow covered area Level 01 on several dates during the year

79 9 Results 9.4 Volume estimations from snow depth interpolation Snow depth soundings performed on the selected section of Level 01 were interpolated using ArcGIS. This interpolation provided continuous information about snow depth of the selected part of Level 01 and therefore an estimation of snow volumes. The most plausible results were obtained with Kriging method. In 2012 the estimated snow volume within an area m 2 amounted to m 3. With an average snow density of 0.59 g cm -3 this quantity corresponds to m 3 of water. In 2013 a slightly larger area of m 2 held m 3 of snow which is equivalent to m 3 of water assuming an average snow density of 0.62 g cm

80 Daily mass balance [mm w.e.] Daily mass balance [mm w.e.] 9.5 Simulation results 9 Results Modeled daily snow melt Three-dimensional models of the snow accumulation indicating daily mass balances are produced by GERM in time steps of one day. This allows a dynamic regard on the snow accumulation as the example in Fig shows. a) b) c) Fig GERM 3 dimensional output for a) 21 June 2012, b) 28 June 2012 and c) 23 June 2012 modeled with the distributed temperature-index model for 3200 m a.s.l. The corresponding day of the year and daily snow melt averaged over the snow surface in mm w.e. are indicated in the top left corner Furthermore, Model experiments were performed for different altitudes using constant lapse rates. Daily mass balance calculated with both models reveals a high negative correlation with daily temperatures as shown in Fig and A negative mass balance represents snow melt and positive mass balances indicate a gain of mass through solid precipitation. For temperatures below 0 C temperature and mass balance become anti-correlated in the temperature-index model. The energy balance model, however, still calculates melt for temperature just below the freezing point. a) Daily air temperature [ C] 0 b) Daily air temperature [ C] Fig Daily air temperatures and daily melt calculated at 3200 m a.s.l. by the temperature-index model for an a) uncovered and b) covered snow surface 66

81 Daily snow melt [mm w.e.] T [ C] Daily snow melt [mm w.e.] T [ C] Daily mass balance [mm w.e.] Daily mass balance [mm w.e.] 9 Results Daily air temperature [ C] R 2 = a) b) Daily air temperature [ C] Fig Daily air temperatures and daily melt calculated at 3200 m a.s.l. by the energy-balance model for an a) uncovered and b) covered snow surface A comparison of the results of the temperature-index model to those of the energy-balance model (Fig. 9.15, Fig. 9.16) shows that both models calculate similar amounts of snow melt. However, snow melt calculated by the energy-balance model shows less daily fluctuations as the temperature-index model. Jun 2012 Jul 2012 Aug 2012 Sep 2012 Okt 2012 Nov TIM_3200_uc TIM_3200_c -20 EBM_3200_uc EBM_3200_c Average daily temperature at 3200 m a.s.l. Fig Daily melt rates at 3200 m a.s.l. calculated with the temperature -index model (TIM) and the energy-balance model (EBM) with (c) and without textile coverage (uc) TIM_3950_uc TIM_3950_c EBM_3950_uc EBM_3950_c Average daily temperature at 3950 m a.s.l. Fig Daily melt rates at 3950 m a.s.l. calculated with the temperature -index model (TIM) and the energy-balance model (EBM) with (c) and without textile coverage (uc). Average daily temperatures 67

82 Initial snow depth [cm] T [ C] 9 Results For temperatures above 10 C melt calculated by the energy-balance model tend to be lower than calculated with the temperature-index model. For temperatures above 10 C this relationship is inverted. The point of no-melt occurs much later in an energy-balance simulation than in a temperature-index simulation Modeled Survival of snow accumulations On the basis of the three-dimensional representations produced by GERM an analysis investigating the longevity of snow accumulations with regard to their initial snow depths was possible. Simulations were run from the beginning of measurements on 20 June 2012 to the end of the year For this purpose several points in the snow field with a defined snow depth were chosen. The time it takes to decrease the snow depths at these points down to 0 cm is the time of survival of a snow mass with the certain initial snow depth. Survivalanalyses for different altitudes, with- and without coverage are represented in Fig and Results in Fig are produced by the temperature-index approach and results in Fig with the energy-balance approach. Both simulations with the energy-balance approach and the temperature-index approach suggest that snow masses with a depth of up to 7 m and regardless of their altitude are not likely to survive longer than the end of August. With coverage, however, their lives can be extended to outlast the summer season and in higher elevations they might even live until the end of the melt period. At that point the volume of snow masses would remain unchanged until the onset of the next melt season c 3450 uc 3450 c 3700 uc 3700 c 3950 uc 3950 c 3200 uc Daily average temeprature at 3200 m a.s.l. -20 Fig Survival of snow masses with regard to initial snow depth on various levels of altitude integrating effects of textile coverage. Modeled with the temperature-index model 68

83 Initial snow depth [cm] T [ C] 9 Results uc 3450 c 3700 uc 3700 c 3950 uc 3200 uc 3200 c 3950 c Daily average temperature on 3200 m a.s.l. -20 Fig Survival of snow masses with regard to initial snow depth on various levels of altitude integrating effects of textile coverage. Modeled with the energy-balance model. Major differences between the two modeling approaches can be observed in the spread of results with altitude. On the base altitude of 3200 m a.s.l. both models still calculate similar longevities for snow masses. An uncovered snow mass of 7 m depth, for instance, is likely to have disappeared by 6 August 2012 after 47 days as simulated by the energy-balance model. The temperatureindex model calculates a complete meltdown of the same snow mass only 3 days later. The results for covered snow masses at the same elevation lie similarly close together. Energybalance simulations calculate a complete meltdown on 16 October 2012 and temperatureindex simulations on 18 October Diverging results are calculated for extrapolations to higher altitudes. Uncovered snow masses at 3950 m a.s.l. of 7 m depth are calculated to last till 17 August 2012 as simulated with the energy-balance approach. By contrast, the temperature-index approach suggests a survival till 29 August The spread of results is significantly amplified when melt is simulated beneath textile coverage. According to the temperature-index model melt on 3450 m a.s.l. decreases rapidly towards the end of summer. Melt processes more or less stop on 18 October Thus, the textile provides sufficient protection to keep a snow mass of 6 m depth from melting down within the melt season. The energy-balance model suggests that even snow masses with snow depths of up to 7 m are likely to disappear completely within the melt season. Only at 3700 m a.s.l. does the energy-balance model suggest a complete survival of snow masses with a depth of 6m. For conditions of minimum snow melt, i.e. at a maximum altitude of 3950 m a.s.l. and under coverage, both models produce the most diverging outcomes. According to the energybalance model only snow accumulations that exceed 550 cm survive long enough to enter the zone of 0 snow melt. With the temperature-index simulation 350 cm are already enough in this respect. 69

84 Runoff [m 3 d -1 ] Runoff [m 3 d -1 ] 9 Results Modeled runoff generated from snow melt The runoff that is generated by snow melt is calculated in GERM for every hour as m 3 s -1. For the purpose of a better illustration this runoff rate is transformed into m 3 d -1. Simulations using both models calculate similar amounts of runoff as a comparison of daily readings calculated for a textile covered conditions between 17 June 2012 and 16 December 2012 in Fig illustrates. Minor differences can be observed during runoff peaks EBM_c TIM_c Fig Daily runoff simulated for textile coverage (c) with the energy -balance model (EBM) and the temperature-index model (TIM) between 17 June 2012 and 16 December 2012 A comparison of simulations with- and without coverage reveals a significant redistribution of runoff under the condition that the whole snow field is covered (Fig. 9.20) TIM_uc TIM_c Fig Daily runoff at 3200 m a.s.l. simulated for covered snow surfaces (c) and uncovered snow surfaces (uc) with the temperature-index model (TIM) between 17 June 2012 and 16 December 2012 Till mid-july runoff from uncovered areas is 2 to 3 times higher than from covered areas. Beyond that point runoff from covered areas is bigger than from uncovered areas. The resultant runoff from covered areas towards the end of August is 5 times bigger than it would be without coverage. On 27 August 2012 GERM calculates a runoff for uncovered areas of 20 m 3 d -1. On the same day the calculated runoff from areas covered with Geotextiles amounts to 108 m 3 d -1. Fig shows runoff in 2012 compared to runoff in 2011 within the same period of time. For a better comparison runoff in 2011 was calculated with the geometry of the snow accumulation in 2012 and run with weather data of

85 Runof [m 3 d -1 ] 9 Results Day of the year Runoff 2011 Runoff 2012 Fig Runoff in 2011 and Runoff in 2011 is simulated with the snow geometry of The day of the year 173 corresponds to 22 June 2011 and 21 June The day of the year 273 corresponds to 30 September 2011and 29 September Till about the beginning of July (day 190) more runoff is calculated in 2011 than in Till around 8 September (day 220) proportions are inversed. In both years the points of complete melt down, i.e. when no more melt is calculated by the model, coincide. Runoff after mid- October is, apart from sporadic runoff peaks, calculated to be close to 0. 71

86 10 Snow distribution and volume modeling 10 Snow distribution and volume modeling 10.1 Approaches As a supplementary attempt to extent results to the entire mountain area an estimation of the total snow volume is necessary. A central element in this estimation is knowledge about the spatial distribution of snow covered areas. For this purpose a LANDSAT satellite scene (Fig. 10.1) was used showing snow covered areas on Mount Aragatz on June 11 th. Fig LANDSAT scene showing snow covered areas on Mount Aragats on 11 June With the general snow cover disappearing wind-blown snow accumulations become visible around this date. In order to classify snow covered areas in the satellite image an Interactive Supervised Classification procedure was applied using ESRI ArcGIS. By selecting training sites on snow covered areas and on bare ground respectively a raster image (Fig. 10.2) was created containing two classes (snow and bare ground). As the contrast between bare ground and snow was large one band was sufficient for classifying. Based on snow depth soundings carried out on the selected cornice together with snow distribution data gathered from the LANDSAT satellite image a rough volume estimation was possible. In order to transfer those estimations to other mountainous regions where no information about snow distribution from satellite images is available the distribution of snow covered areas was modeled with two different approaches based on the GDEM. The first approach uses ArcGIS to derive snow depths from a statistical relationship between snow distribution, learned from the LANDSAT image, and topographical features, derived from the GDEM. A more sophisticated approach is an automated approach based on the process of redistribution of snow by wind action designed by Dr. M. Huss. For this approach a uniform snow cover for the whole mountain is assumed as a starting point. Wind action from a defined direction redistributes snow depending on topography. A simple temperature-index model is applied in a second step to simulate the disappearance of snow. 72

87 10 Snow distribution and volume modeling Fig Snow covered areas and bare ground classified from a LANDSAT scene of Mount Aragats on 11 June

88 Average Depth [cm] 10 Snow distribution and volume modeling 10.2 Satellite image based volume estimation This approach is aimed at the estimation of snow volumes by extrapolating measurements in one snow field to the entire snow covered area of Mount Aragats. The spatial distribution in this approach is adopted from the classification of the satellite image. Snow depths were modeled using ESRI ArcGIS. A Euclidian distance function that was applied to the edges of all snow accumulations represented increasing snow depths with distance from the edge Distance [m] Fig Average snow depths with distance from the edge The increase in snow depth with distance from the edge is, however, not limitless. Instead snow depths rather increase up to a certain value and remain at this level despite increasing distances from the edge. In order to scale the Euclidian distance values correspondingly snow depths measured in the selected snow cornice was used as a reference. By sampling both Euclidian distance and snow depth at each point of the snow cornice a function of average snow depth at a given distance could be established (Fig. 10.3). This function shows a highly linear relationship between distance from the edge and snow depth. At 40 m from the edge, however, snow depths seem to level off and remain constant at 4 m. Based on this function Euclidean distances all over the mountain area were scaled in a way, that snow accumulations linearly become deeper up to a depth of 4 m. At 40 m from the edge and beyond snow depths remain at 4 m. For higher elevations this relationship might be different. For a lack of data the relationship between distance and snow depth was transferred to the whole mountain area. 74

89 10 Snow distribution and volume modeling 10.3 ArcGIS-based statistical distribution modeling of snow covered areas This approach represents an attempt to reproduce snow accumulations that are formed by wind action on Mount Aragats Step 1 Decreasing resolution of the DEM Wind-blown snow accumulations are known to form along sharp edges in topography, i.e. a convex or positive curvature of the terrain. These curvatures can be derived from a digital terrain model. As the available ASTER GDEM is too irregular and noisy it was necessary to aggregate 4 pixels each and thus decreasing the resolution from about 27 x 27 m to about 109 x 109 m. This automatically leads to a smoothing of the surface Step 2 Calculating curvature After the DEM had been smoothed sufficiently curvatures could be calculated using the ArcGIS Spatial Analyst (Fig.10.4). Fig Curvatures from ASTER GDEM above 2500 m 75

90 Step 3 Extracting positive curvatures 10 Snow distribution and volume modeling As only convex features were interesting negative curvatures were discarded leaving only positive land forms Step 4 Skeletization In order to retrieve edges as single lines the morphological filter Thinning provided in ArcGIS was performed. As described in HUSSAIN 1991 objects are successively stripped of their outer layers to expose their skeleton, a continuous frame with the width of one pixel. This frame could then be transformed into a line-shape file as presented in Fig Fig Edge lines obtained by skeletization of positive curvatures Step 5 Buffering edge lines In order to simulate snow accumulation around the edge lines a buffer was applied around each line feature. As the width of snow accumulations varies with altitude the width of the buffer was not held constant. In order to quantify the variation of snow accumulation with altitude pixels holding snow were counted in altitudinal belts in intervals of 100 m. The mountain is, however, not entirely symmetrical. That means that the number of pixels by which an altitudinal belt is represented may vary for different elevations. Thus the snow distribution with altitude has to be expressed by the ratio of snow pixels per pixel of the DEM (P SNOW / P DEM ) as represented in Fig From this distribution a 3 rd degree polynomial 76

91 ratio snow / underground ratio snow / underground 10 Snow distribution and volume modeling regression curve could be calculated. This curve characterizes most of the snow distribution. Between 2600 m and 2800 m, however, it yields negative values for elevations below 2500 m. So as to bypass this problem values between 2500 and 2900 m were replaced by a 2 nd degree polynomial regression curve (Fig.10.7). Hence the buffer width W [m] at a given altitude h [m] was calculated as a fraction of the maximum width (W max = 800 m) as follows: If h 2900 then ( ) If h > 2900 then ( ) (10.1) The resulting buffers are depicted in Fig y = x x x altitude [m a.s.l.] Fig General regression function calculated for the Change in snow cover with altitude y = 4E-07e 0.004x Fig Regression function calculated for the change of snow cover below 2900 m altitude [m a.s.l.] 77

92 10 Snow distribution and volume modeling Fig Buffer around edges of positive curvature in accordance with the observed snow distribution Step 6 Subtracting certain aspects It is obvious that the isotropy of the snow cover increases with altitude. Snow fields strictly face a certain cardinal direction at lower altitudes and their orientation becomes more and more omnidirectional with increasing elevation. In order to account for this effect an aspect mask was created (Fig. 10.9a). On the lowest level, on 2500 m, only aspects facing NW were included. The angle was linearly widened up to 4000 m until all aspects from 0 to 360 were included (Fig. 10.9b). 78

93 10 Snow distribution and volume modeling Fig. 10.9a Mask to exclude certain aspects Fig. 10.9b Buffer without excluded aspects Step 7 Subtracting steep slopes In a further step slopes that are too steep to hold snow were subtracted from the mask, which produced a final mask of constraints within which snow accumulations were likely to occur. Counting snow pixels and reading the respective slope inclination for each pixel provided information about the maximum slope inclination where snow accumulation occurs. These slope angles were found to get bigger with altitude. Tab is a compilation of those inclinations in different altitudinal belts. Fig shows the final mask of constrains indicating likely areas of snow accumulation. Tab Maximum slope inclination for snow accumulation Altitudinal belt [m] Maximum slope inclination [ ] Above

94 10 Snow distribution and volume modeling Fig Final mask of constraints for areas that are likely to hold snow Step 8 Snow depth estimations As a supplement snow depths were estimated based on the spatial distribution in LANDSAT images. By reading the aspect of each snow pixel from the underlying GDEM it was possible to establish statistics of the orientation of snow fields in the different altitudinal belts (Fig ). The number of hourly wind directions during the hydrological winter of 2011 / 12 ( till ) is depicted in Fig It indicates the direction in which the wind was blowing. All wind direction data below a wind-velocity threshold of 1 m s -1 were discarded to avoid random wind data during windless times. As expected snow covered areas face away from the prevailing wind direction at lower altitudes. At higher altitudes, however, a shift towards the opposite direction can be observed. This clearly does not represent the true situation, but rather expresses the asymmetry of the landscape. In order to correct for this asymmetry a correction factor had to be calculated. For this purpose the relative number of GDEM-pixels at every degree of the cardinal direction was determined for the respective altitudinal belt. The correction factor was then calculated for every degree as the deviation of the number of GDEM-pixels from an equal distribution of aspects as ( ) ( ) (10.2) where CF(C) is the correction factor for a cardinal direction C. D GDEM is the relative number of pixels in a certain cardinal direction. D eq is the equal distribution of aspects (D eq = 1 / 360). The correction factor was simply added to the relative number of snow pixels facing the corresponding cardinal direction. The result of this correction can be observed in Fig. x2. 80

95 10 Snow distribution and volume modeling Fig Orientation of a relative number of snow pixels in different altitudinal belts; corrected (red) vs. uncorrected (blue) 81

96 10 Snow distribution and volume modeling In order to use the corrected frequencies of snow coverd areas in the various aspects as a scaling factor for snow depth all values had to be stretched to the value of highest frequency in each of the altitudinal belts. Since adequate measurements, except for the altitudinal belt between 3001 and 3250 m, lacked maximum snow depths were assumed as indicated in Tab Tab Maximum snow depths in different altitudinal belts Elevation [m a.s.l.] Maximum snow depth [cm] above Snow depths estimated in dependence of aspect were then obtained by multiplying the scaling factor with maximum snow depths. 0 values and negative ones had to be substracted afterwards Step 9 Confining snow covered areas In step 8 snow depths were simulated for all places on the mountain. With the mask of contraints created in step 7 snow fields were confined to the place of their highest likelyhood of occurrence Step 10 Decreasing snow depths at the edges In order to account for snow depths to become shallower towards the edges a euclidean distance was calculated. From 0 to 400 m from the edge snow depths calculated in step 8 were scaled linearly, from 0 % of the calculated snow depths at the edge to 100 % of the calculated snow depths in a distance of 400 m. Beyond 400 m calculated snow depths were kept as they had been determined in step 8. 82

97 10 Snow distribution and volume modeling 10.4 Automated process-based distribution- and volume modeling of snow covered areas This model is executed using the programming language IDL (Interactive Data Language). The code was written by Dr. M. Huss in Input parameters The necessary input parameters for the simulation of snow redistribution are exclusively derived from the GDEM. Such as the ArcGIS-based approach this approach derives from the GDEM information about curvatures to identify topographic edges, aspects to define leesides and slopes to exclude too steep or to shallow slope angles. For the melt of snow masses a simple temparature-index model as described in chapter 2 is used which requires temperature and precipitation data as input Model description As a starting point the model uses a defined snow depth. In this case 400 mm w.e. are assumed for an altitude of 3000 m a.s.l.. Snow depth for higher elevations are extrapolated using a constant gradient of 15 mm/ 100 m. The final snow depths is determined by the weight of different parameters. Decisive for the deposition of snow masses are sharp edges in topography. These are defined by a critical positive curvature. On the base altitude of 3000 m a.s.l. this critical curvature is highest and decreases by a certain gradient with altitude. It may however not drop below a certain curvature minimum. In the lee-side of the edges deposition of snow takes place. The lee-side is defined as opposing a constant wind direction. The magnitude of deposition decreases linearly to an angle deviating by 30 % from the direction of the lee-side. Various experiments with different wind directions were carried out. However, the biggest resemblance with the actual snow distribution pattern as observed in the satellite image was obtained with wind direction of 280 NWW. This wind direction corresponds to the direction found in the statistical evaluation of meteorological data. Snow accumulation only occurs to a certain distance from in the downwind side of edges. In order to account for this a bufferzone of 200 m is created around edge lines. As snow accumulations become larger with altitude these buffer zones are extended with increasing altitude using a constant lapse rate. The analysis of snow distribution in the LANDSAT image revealed decreasing occurance of snow accumulations for too low such as for too steep slope angles. Only within a certain range of slope angles maximum snow accumulation could be observed. In the model slope angles were considered by introducing thresholds. From the lower threshold of 15 down to 0 slope inclination snow accumulation linearly drops down from 100 to 20 %. From the higher threshold, which is set to 40, up to the maximum slope inclination of 60 accumulation linearly decreases to 0. The accumulation in dependance of slope angles is illustrated in Fig

98 Snow accumulation weight 10 Snow distribution and volume modeling Fig Weightening of snow accumulation in dependance of slope angles Slope angle [ ] Apart from wind driven snow accumulations a smaller share of snow stays undisturbed by wind action outside the buffer zones. This undisturbed snow precipitation is goverened by an additional parameter. In order to iteratively remove non-wind driven snow cover a simple temperature-index model is run in time steps of 10 days. 84

99 10 Snow distribution and volume modeling 10.5 Validation of Snow distribution modeling The performance of both the ArcGIS- based and the automated distribution model was validated by means of an optical comparison with the actual snow distribution obtained from the LANDSAT image (Fig ). The LANDSAT image was recorded on 26 June A Look at the meteorological data shows that temeperatures at 3200 m a.s.l. significantly rise obove the freezing around 01 May. This time differnce implies a melt period of 56 days. Hence, snow distribution calculated by the autmoated approach after 50 days of melt was used. b) a) Fig Snow covered area classified form LANDSAT image in comparison with a) automated approach after 50 days of melt and b) ArcGIS-based approach Both models well immitate the general distribution pattern with broad istropic snow covered areas in higher elevation and narrow tongues tracing edges in topography that oppose the prevailing wind direction. Both models overestimate the snow covered area significantly. Especially snow cornices along the edges in lower elevations are reproduced as too large. The area modeled by the ArcGIS-based approach ammounts to km 2, wheras the area determined in the LANDSAT image is, with 92.8 km 2, considerably smaller. A zoom-in to the area of investigation reveals significant differences in terms the reproduction of details as Fig shows. The automated approach shows a much better performance in the reproduction of details than the ArcGIS-based approach. 85

100 10 Snow distribution and volume modeling b) a) Fig Snow covered area within the area of investigation classified form LANDSAT image in comparison with a) automated approach after 50 days of melt and b) ArcGIS - based approach 86

101 10 Snow distribution and volume modeling 10.6 Results of distribution- and volume estimations Volume estiamtes calculated with the three approaches differ quite significantly form one another. Estimated snow volumes were transformed into water volume using the constant snow density of 0.59 g cm -3 measured in The approach based on the satellite image yields a snow volume of mio m³ of snow corresponding to mio m 3 of water distributed over a snow surface of 92.8 km 2. An extract of the modeling is shown in Fig Fig Modeled snow depths based on a satellite image The snow volume calculated with the ArcGIS-based approach amounts to mio m 3 of snow. Its equivalent in water is mio m 3. Fig shows how modeled snow depths are distributed over the snow covered area. Fig ArcGIS-based modeled snow depths 87

102 10 Snow distribution and volume modeling The automated approach calculates snow volumes for each time step of the melt modeling. Snow volumes after 50 days of simulated melt were chosen to be comparable to the other approaches for the length of the melt period. Snow volumes after that time amount to mio m 3 which corresponds to 79.0 mio m 3 of water. Fig shows the spatial distribution of snow depth based on the automated approach after 50 days of simulated melt. Fig Modeled snow depths based on the automated approach after 50 days of melt simulated with a simple temperature-index model Tab summarizes snow volumes as calculated by the three different approaches. Tab Snow volumes calculated by different approaches in comparison Approach Volume [mio m 3 ] Satellite image-based ArcGIS-based Automated

103 11 Discussion 11 Discussion 11.1 Discussion about weather data A comparison of weather data from 2013 and before 2013 reveals some inconsistencies. Solar radiation readings during the summers of 2011 and 2012 are 2 times smaller than the ones recorded in summer of 2013 (Fig. 5.8). Similarly conspicuous readings can be found within wind direction data. Here wind directions measured in winter differ by approximately 90 from wind directions measured in winter (Fig. 5.10). According to collaborators of the Cosmic Ray Station both the wind vane and the UV-sensors were readjusted in the summer of Exact information in order to correct the data can, however, not be provided at the moment. Since both wind direction and solar radiation were not directly used to produce results a data correction is not essential for this study. 89

104 11 Discussion 11.2 Discussion about the accuracy of ablation measurements and snow density measurements Either due to vandalism or due to limited time in the field, ablation time series have either a limited length or a limited temporal resolution, which is a major hindrance for the proper calibration of the melt models. As Fig. 9.4 shows significant changes in the height of the snow surface may occur in the course of one day. On larger time-scales of weeks to months, as for instance on a glacier the exact day time of the ablation measurement does not play an important role. However, for shorter time series the day time of data acquisition is rather important. During field visits an exact recording of the day time was neglected. This failure increases the uncertainty of ablation measurements by 4 cm. Including uncertainties of the measurements themselves, the total uncertainty for ablation measurements is thus 6 cm. In the field no differentiation of the snow profile with regard to density could be observed and also data series do not show clear trends but rather seem to fluctuate randomly. This suggests deviations in measured snow density to be a result of inaccuracies of field methods. The cylinder used to extract snow from the profile was never completely filled in order to avoid exceeding the capacity of the spring balance. This made an exact determination of the amount of snow contained in the cylinder difficult. Using a bigger snow volume by filling the cylinder completely or even using a bigger cylinder and weighing its contents separately would minimize the impact of an inaccurate definition of snow volumes. 90

105 11 Discussion 11.3 Discussion about patterns in perimeter mappings The recurring patterns in snow distribution that can be observed in Fig. 9.7 to 9.9 indicate annually repeating and constant processes. An analysis of wind direction in winter time indeed exhibits a strong anisotropy. The prevailing wind direction, however, was found to change from year to year as a comparison between the winters of 2011 and 2012 shows (Fig. 5.10). This difference in hibernal wind directions might be the cause for minor differences in snow accumulation. As these differences mostly consist in tongues reaching down from the snow cornice, they might also be explained by ruptures of the snow cliff that trigger a release of snow in the form of avalanches. Repeated mappings such as those performed on the Vishab cornice (Fig. 9.10) show distinct velocities in snow area diminution according to their topographic exposition. On a larger scale, including a greater number of different topographic positions such as on Level 01 (Fig. 9.11), those positions emerge that are most favorable both in terms of snow accumulation and snow melt. In other words, the smaller the mapped snow cornices the smaller the deviation from the optimal topographic position for the preservation of natural snow accumulations. 91

106 Runoff [m 3 d -1 ] Precipitation rate [mm h -1 ] 11 Discussion 11.4 Discussion about the melt-model outputs A comparison of daily melt calculated by the distributed temperature-index model (Fig. 9.9 and 9.10) show an immediate decorrelation of readings as soon as temperatures drop below the freezing point because of the absence of melt. The energy-balance model, however, calculates melt even below the freezing point. This discrepancy is established in the treatment of air temperature in the calculation of melt. While the temperature-index model gives priority to air temperature and sets melt preemptory to 0 for temperatures below 0 C the energy-balance model implements air temperature as just another parameter. Short-and long-wave energy fluxes are treated as equal constituents when it comes to calculating melt energy and thus ablation may in this model even occur under freezing conditions. This difference in performance for temperatures below the freezing point has a significant impact on the simulation of longevity of snow accumulations as Fig and 9.14 demonstrate. The big difference between both simulation approaches for covered high-altitude accumulations are thus explained by the differences in the performance below 0 C. In a nutshell, both models produce comparable outputs for uncovered conditions regardless of their elevation, since no snow accumulation is expected to live long enough to be exposed to freezing air temperatures over a longer period of time. For more persistent covered accumulations above the base altitude the longevity of accumulations largely depends on the choice of the model. However, adequate means to validate the long-term performance of the models are lacking. Nevertheless, model outputs of the two approaches coincide in their general statement and define a range of probable melt scenarios. Runoff rates calculated by both models are characterized by prominent peaks. As Fig shows these peaks are to a high probability connected to precipitation events. During snow melt, while reservoirs are full, the water from precipitation adds up to the melt water and triggers almost instantaneous increases in runoff. After snow accumulations have disappeared completely reservoirs increasingly drain until they are emptied completely. Precipitation events on empty reservoirs events such as on 27 October show a minor instantaneous impact on runoff due to a reaction of the fast reservoir. The majority of runoff however occurs with a delay after slow reservoirs have been replenished. The drainage of reservoirs can be observed succeeding a series of smaller precipitation events that last from 08 till 09 September Precipitation Runoff Fig Daily runoff and precipitation rates Simulations with Geotextile show a significant redistribution of runoff compared to bare snow surfaces. Fig.11.2 illustrates the magnitude of this effect as the difference between runoff from covered and uncovered areas. Clearly three phases are discernible that characterize the effect a Geotextile has upon runoff. 92

107 Runoff [m 3 d -1 ] complete meltdown uncovered area complete meltdown covered area 11 Discussion In the first phase (Ia) melt rates in uncovered exceed those in covered areas. As a consequence runoff from uncovered areas is calculated to be 800 m 3 d -1 higher compared to covered areas. The higher melt rates lead to faster shrinkage of snow volumes of uncovered accumulation. This leads to a faster decrease in runoff from uncovered snow accumulations as compared to covered ones until at one point runoff from covered areas exceeds runoff from uncovered areas. During phase Ib the difference between runoffs in favor of runoff from covered snow accumulations steadily increases. The surplus of runoff from covered areas gets bigger until the entire uncovered snow accumulation has melted away around 03 August. This is the point where the snow accumulation covered with Geotextiles produces the biggest effect. The distributed temperature-index model calculates 200 m 3 d -1 more runoff from covered areas during the redistribution peak. After the complete disappearance of uncovered snow masses the difference between the two runoff configurations diminishes as runoff from covered areas decreases with decreasing snow volumes and falling air temperatures (phase II). This process goes on till all remaining snow masses under textile coverage have disappeared Ia Ib II III Uncovered Runoff - Covered Runoff Fig Difference between runoff from covered and uncovered snow accumulations on 3200 m a.s.l.. simulated with the distributed temperature-index model 93

108 11 Discussion 11.5 Discussion about the efficiency of Geotextiles, their applications and alternative snow preserving methods Fig illustrates the significant redistribution of runoff as an effect of coverage with Geotextile yielding 200 m 3 of water on days when natural uncovered snow is almost completely gone. Nevertheless, the investigated effects concern a complete coverage of the entire test area, which corresponds to m 2. In 2012 only 3 % of this area: 600 m 2 were covered with Geotextiles. The effects of coverage on such a small scale are correspondingly lower providing 7 m 3 d -1 during the redistribution peak. Hence, for one 1 m 2 of Geotextile maximum difference in runoff redistribution amounts to 0.01 m 3 d -1 or 18 l d -1. Snow ablation measurements demonstrated the melt reducing effect of Geotextiles. By contrasting daily melt rates of covered and uncovered areas and their integration over a defined area it is possible to determine the water saving capacity of Geotextiles. With a melt reduction of 57 % that was found during the measurements Geotextiles are capable of saving 0.04 m 3 of water m -2 d -1. For one piece of Geotextile with an area of 250 m 2 this means that 9 m 3 less water melts away every day. The maximum difference between covered and uncovered snow volumes can be found at the beginning of August when the last remainders of uncovered snow disappear. The water that is saved until this day amounts 1.94 m 3 which corresponds to m 3 for on piece of Geotextile. Regardless of the area covered a protected snow surface will always persist considerably longer than it would without any coverage. This circumstance gave rise to the idea of preserving a strip of snow to create a piste for skiing in autumn when most tourists visit the Aragats area. This strip of snow is supposed to be as long as possible reaching from the south saddle all the way down to the Lake Vishab as shown in the terrain model in Fig N Fig Hypothetical skiing piste, symbolized by a white strip, reaches from the south saddle at 3700 m a.s.l. down to Lake Vishab at 3200 m a.s.l. 3D visualizations based on ASTER GDEM. elevations are exaggerated With an assumed width of 20 m and a length of 3.7 km this piste would cover an area of m 2. An area of that size would correspond to coverage with ca. 300 Geotextiles. From a financial point of view it is doubtable weather the revenues of the skiing business would make up for the enormous costs of the Geotextiles that would have to be imported from Switzerland. Furthermore, at the moment, there are no adequate machines available on Mount Aragats to place those textiles in an efficient way. There is an alternative to covering a continuous strip of snow to create a skiing piste. By the end of June snow fields still cover all the way up to the southern peak of Mount Aragats in large isolated patches. These patches are separated by narrow corridors of rock. If textile 94