Challenges in Hydrology of Mountain Ski Resorts under Changing Climatic and Human Pressures

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1 Challenges in Hydrology of Mountain Ski Resorts under Changing Climatic and Human Pressures Carmen de Jong (1), Thierry Barth (1)/(2) (1) The Mountain Institute, UMS Mountains CNRS 346, Batiment Belledonne, University of Savoy, Le Bourget du Lac, France (2) CISM, Master Gaia, University of Savoy, Le Bourget du Lac, France INTRODUCTION Hydrology in mountain areas is particularly important with regard to new and increasing pressures in the upper catchments [1]. Climate change, causing phenomena such as decrease in precipitation in the western Alps over the last decade and increase in water abstraction for tourism and artificial snow in particular [2], requires intensive monitoring in different high altitude zones. On the one hand, measurements of classical hydrological components, such as precipitation, discharge, evapotranspiration and groundwater are rare and on the other hand, terrestrial photogrammetry and remote sensing techniques are not yet purposefully applied [3]. For example, to monitor the expansion of ski runs with artificial snow in the Alps and their impacts on the discharge of mountain torrents, a combination of hydrological monitoring, field measurements and high resolution remote sensing is a requirement. Radar methods should provide useful information to monitor the filling levels of new reservoirs built to capture water for the production of artificial snow and to differentiate ski runs with natural and artificial snow. In addition, ablation of prolonged snow cover from ski runs with artificial snow can be derived from terrestrial photography. Such data is important for development and validation of hydrological models. In the test area of the Arcs, Bourg-Saint-Maurice in Savoy, France, a local hydrological model is run to model the impacts of artificial snow on torrent discharge under different scenarios with relation to the construction of a new, large slope reservoir and interbasin water transfer [4]. The construction of such reservoirs is increasing at high altitudes, where surface and groundwater is naturally limited. Since mountain areas are ecologically highly fragile, hydrological modifications require detailed temporal and spatial analysis to avoid water conflicts related to winter tourism [5]. Remote sensing techniques and hydrological modeling should become essential components in monitoring environmental change associated with slope water reservoirs and artificial snow on ski runs. STUDY AREA The study site examined comprises the Arcs ski area in the valley of Tarantaise near Bourg-Saint-Maurice in Savoy, France (Fig. 1). The region consists of two major areas, the Arcs 2 and in particular the north-west slopes of the Arcs The area investigated lies within the catchment of the Arcs which consists of approximately 35 km or 513 hectares of ski runs extending up to 3226 m in altitude of which 15% or 7 hectares are covered by artificial snow. Artificial snow production is increasingly necessary to ensure operational ski runs. Under the current reality of climate change, there are less snow secure days, the amount of snowfall is decreasing markedly and temperatures have increased considerably. Thus, average annual temperatures have increased by 2 C (from approximately C) in only 3 years whereas the annual rainfall of 1 mm has decreased by 8% over the last 5 years [1] and snowfall has decreased from more than 8 m to 4 m in only 3 years [4]. Dominant geology is schist and thin layered metamorphosed sandstones with some coal veins with gneiss and quartzite towards the summits [4]. The geomorphology consists mainly of glacial deposits, such as moraines and extensive talus slopes below the rocky summit slopes. The ski runs generally extend along steep slopes above the valley shoulder and below the summits. The hydrogeology is strongly influenced by the prevailing

2 geomorphology and geology and consists mainly of direct surface runoff, infiltration into subsurface strata, infiltration into the groundwater and circulation through fractured zones. Six individual catchments with catchment areas between km2 are investigated, all of which drain into the River Isère. The hydrology of these streams is characterised by flashy regimes. Simulations of the hydrological reactions of the torrent of Preissaz in 1977 in the pre-tourism era compared to 23 show a doubling of flood peaks [6]. This is due to the increase in impermeable areas related to urbanisation of ski resorts, in particular the construction of roads, parking places and ski runs (Fig. 5). Since 1989, there has been an exponential increase in the consumption of water for artificial snow production. The production of artificial snow is dependant on the availability of local water resources in the region. Snow making has been limited so far by unfavourable climatic conditions, i.e. temperatures that lie above -3 C or by a lack of water availability due to the provision of potable water to tourists and local inhabitants during the winter holidays. For the 58 hectares of ski runs covered by artificial snow, approximately 2 m 3 of water is consumed annually. A new artificial slope water reservoir with a capacity of 4 m 3 is being constructed in the adjacent basin of Pissevieille at Adret des Tuffes to ensure water storage and an annual interbasin water transfer to Arcs in the order of 55 m 3. Thus, in future, it is planned to cover 123 hectares of ski runs by artificial snow. Catchment of Pissevieille Snow water reservoir of Adret des Tuffes Arc Arc 18 Bourg-Saint-Maurice Arc 16 Fig. 1 Study site of the Arcs ski area near Bourg-Saint-Maurice in Savoy, France with the construction site of the water reservoir for snow making, Adret des Tuffes. The numbers 1-6 indicate the hydrological sub-basins modeled,where 1 = Torrent des Moulins, 2 = Torrent de la Ravoire, 3= Torrent de l Eglise, 4 = Torrent de Saint Pantaleon, 5 = Torrent des Villards and 6 = Torrent de la Preissaz.

3 METHODOLOGICAL APPROACH Field campaigns In mountain areas, the need for careful monitoring of hydrological data is often not as obvious as for the lowlands that are more intensively populated. However, ski resorts are increasingly approaching the dimensions of medium sized cities, with more than 5 tourists frequenting these high altitude areas during peak times in winter. In these environments, water is not ubiquitous in the winter since it is mostly frozen or prevalent at the subsurface. Common difficulties in measuring the different components of the hydrological cycle include the representativeness of sites in remote locations and the effects of extreme weather conditions [3]. However, it is important to increase the spatial density of representative meteorological stations in the headwater catchments. Since the winter period coincides with the most intense period of water abstraction, both for hydroelectricity, tourists, artificial snow and the local communes, a precise knowledge of the different hydrological quantities is essential. Artificial slope water reservoirs constructed for snow making purposes are mostly alimented by water from other basins or from the rivers below, thus significantly modifying stream discharge, either by increasing or decreasing it within the same basin. The impacts of such large scale local modifications to the water cycle, in particular river discharge, should be monitored continually beneath each modified torrent. Since this is not the case, the monitoring of hydrological change and the validation of hydrological models is very cumbersome. Hydrological modelling The GR Model In order to better estimate the impacts of water transfer from the basin of Arc 2 to the basin of Arcs 18, it is essential to understand the hydrological regime of the torrents of the basin of Arcs Therefore, the discharge of the six torrents in this catchment is modelled using a global, conceptual, rainfall runoff model, the GR (Genie Rural) model developed by the CEMAGREF and adapted by Barth in 27 [4]. This is a low parameter model running at daily time steps, developed by Nasciemento in 1995 [7] and modified by Edijatno et al in 1999 [8]. It considers the catchment as a black-box consisting of a number of reservoirs, with one known input, precipitation and one known output, discharge. The main input parameters include the capacity of the water production / soil reservoir, the co-efficient of subsurface exchange, the capacity of the routing / gravitational flow reservoir and the base flow time. The optimisation of the modules of the GR model is difficult, since no discharge measurements are available for the different torrents. Therefore, the parameters are optimised mathematically and the return periods of similar discharge are classified according to the return period of similar precipitation events. This allows the daily and maximum discharge to be calculated for the different torrents. A special semi-distributive module for the simulation of snowmelt was developed by Barth [4] based on the degree-day method and classified according to 1 m iso-altitudinal bands. It consists of input parameters temperature and precipitation, daily snow melt and contribution of artificial snow. The surfaces covered by artificial snow are subdivided into different altitudinal bands together with the amount of water necessary to produce the snow. The altitudinal bands with artificial snow are adapted to those with natural snow, so that the excess in water produced by snowmelt from artificial snow is added to the natural torrent regime. A second module based on the same principles was developed for flood discharge functioning at an hourly time step. The hydrological model is calibrated according to the discharge data of the adjacent torrent Pissevieille (Arcs 2) that had a gauging station between related to a water diversion by EDF (French Electricity Works) for hydropower. The model was calibrated both on the basis of discharge data, on the basis of the specially developed snow module and on the basis of average data. The best calibration results were obtained from the method based on the hydrological data. Minimal flow was consistently underestimated in the calibration exercise, due to the model assumption that negative temperatures are associated with snow. Also, artificial snow campaigns are modelled independently of temperature, whereas in reality snow can only be produced under dry conditions at temperatures below -4 C. Hydrological impacts of artificial snow The hydrological modelling results show the general impact of artificial snow, both on a monthly basis throughout the year and for floods on an hourly basis. Comparison of the monthly discharge for the 6 torrents (Fig. 2 a-f) showed that the difference between the natural and artificial snow melt-induced discharge was negligible for the autumn and winter months

4 (between September February). The largest differences (close to one third of the total discharge) were modelled for the summer months, notably in July and August. A maximum difference of 33.8% was noted in July for the torrent of Les Villards, whose catchment has more than 4% impermeable surface due to ski infrastructure [6]. Thus stream discharge is increased by nearly one third in the summer months. In conclusion, the seasonal impacts of artificial snow extend far into the summer season and are most obvious between the months of March and August but in particular after May. Increase in discharge below ski runs due to artificial snow production has been described previously [9]. Since stream discharge is correlated with stream velocity and sediment transport, this increase in discharge is likely to cause more erosion [1]. a) b) monthly mean discharge (L/s) Torrent des Moulins 13,7%,%,%,%,% -,5%,%,% -1,5% 6,9% -,6% 11,1% Oct. Nov. Dec. Jan. Feb. Mar ch April May June July Aug. Sept natural discharge (L/s) artificial discharge (L/s) difference in discharge (%) monthly mean discharge (L/s Torrent de la Ravoire 35 15,% 3 1,4% ,2% 1,%,%,% -,2%,% -,1% -,2% -,8% -1,3% 5 natural discharge (L/s) artificial discharge (L/s) difference in discharge (%) c) d) Torrent de l'eglise Torrent du Saint-Pantaléon monthly mean discharge (L/s 4 26,4% ,1% ,%,%,%,%,% -2,%,6%,% 1 5-4,8% -3,4% natural discharge( L/s) artificial discharge diffrence in discharge (%) monthly mean discharge (L/s) 35 32,2% 29,9% 3 23,9% ,6% 15 1,% -,5% -,1%,% -,5%,% -2,4% -3,5% 5 natural discharge (L/s) artificial discharge (L/s) difference in discharge (L/s) e) f) Torrent des Villards Torrent de la Preissaz monthly mean discharge (L/s) ,5%,% -,7% -,2% -,2% -,7% -2,8% 1-4,1% 5 22,1% 33,8% 27,1%,% natural discharge (L/s) artificial discharge (L/s) difference in discharge (%) monthly mean discharge (L/s) 4 14,1% 35 11,6% 3 8,8% ,9%,%,%,% -,1% -,4%,% -1,1% 1-1,9% 5 natural discharge (L/s) artificial disacharge (L/s) difference in discharge (%) Fig. 2 Hydrological regime of the 6 torrents of Arc 18-2 indicating the natural reference discharge in grey (in l/s) and the discharge under the impact of artificial snow in stippled grey. The percentage difference between the natural and modified discharge is shown with the black curve. The torrents include a) Moulin (1), b) La Ravoire (2), c) l Eglise (3), d) Saint Panteleon (4), e) Les Villards (5) and f) La Preissaz (6).

5 The possible evolution of the hydrograph under different climate scenarios is illustrated in Fig. 3. Three different possible evolutions were applied which include weather extremes such as very warm and very cold winters or springs and very wet or very dry winter/spring seasons. Possible evolution 1 assumes large quantities of artificial snow production which causes a delay in the peak snowmelt and prolongs snowmelt by at least one month. Possible evolution 2 assumes large quantities of natural snow, a very warm spring and the simultaneous melt of artificial and natural snow. Possible evolution 3 assumes little natural snow in winter and a very cold spring with little snowmelt and low discharge. For all scenarios, the first part of the hydrological year (between October to February) shows a slight decrease in discharge (by up to 2%) caused by natural snowmelt at altitudes between 15-2 m in the absence of artificial snow melt on the ski runs (Fig. 4). In March and April, at the beginning of the melt season, the decrease in discharge is more marked, and lies between 3-5% due to the delaying effects of artificial snowmelt. This causes a decrease in discharge with relation to the natural state in the absence of artificial snow. Between April to June during the peak snowmelt season there is a simultaneous net increase in discharge by between 1-2 %. During the final melt period (from June to August) a systematic increase in discharge can be observed due to the snowmelt from residual artificial snow remaining on the ski runs. During mild winters, the hydrological impact of artificial snow is strongest. However, since in this case water reserves on the slopes are limited due to a thin snow cover, discharge remains relatively low. Inversely, during snow rich days, artificial snow extends the length of the peak melt by several weeks. For all scenarios, there is a 2-3% increase in discharge between the months of May to August and between 3-5 % decrease in discharge for the months of February to April. - 1 to -2 % - 3 to -5 % +2 to +3% Average monthly dicharge natural discharge (L/s) possible evolution 1 possible evolution 2 possible evolution 3 Fig. 3. Possible evolution of the hydrograph under the impact of artificial snow for basins 1) to 6) combined. The black line indicates the natural discharge and the red, green and blue curves show various other scenarios The impacts of artificial snow on flood events vary significantly on an hourly basis depending on the type of torrent, the magnitude of discharge and its relation with the magnitude of flood discharge. Several years with extreme meteorological events were identified and the reaction of the basins was studied according to different extreme climatic scenarios. The impacts of artificial snow on the flood flow regime are most considerable for the torrents of Les Villards (5) and Saint Pantaleon (4), (Fig. 4) which coincidentally have the highest impermeable surfaces (more than 4% of the catchment) due to ski infrastructure. For the extreme flood of 12th June 23, the natural discharge is shown in relation to the discharge modelled from the impacts of artificial snow melt. In June, it is assumed that all natural snow has already melted but that extensive surfaces covered by artificial snow still exist. The extreme precipitation event causes widespread and rapid melt of the artificial snow due to prevailing high temperatures (nearly 25 C). The discharge increases from m3/s due to the effects of artificial snowmelt and thus by approximately 18% for Les Villards and 15% for Saint Pantaleon.

6 a) Villards b) Saint-Pantaléon discharge (m 3 /s),8,7,6,5,4,3,2,1 Pluie Débit naturel Débit artificiel rainfall natural Q artificial rainfall (mm) Fig. 4 Precipitation, extreme flood of 12 th June 23 (blue line) and reconstruction of impacts of artificial snow melt (yellow line) for the torrent a) Villards (5) and b) Saint Pantaleon (4). discharge (m 3 /s),7,6,5,4,3,2,1 Pluie Débit naturel Débit artificiel rainfall natural Q artificial rainfall (mm) The critical period for maximum impact of artificial snow on pre-existing flood flows is between mid-may to mid June. This period neither coincides with the preferred period of debris flow triggering after mid-june to July, nor the occurrence of late summer storm events in September to October, nor the creation of natural snowmelt flood events in March to April. Thus it is unlikely that melt from artificial snow can trigger a debris flow. However, in the long term the overall increase in discharge from artificial snowmelt is likely to enhance the already existing erosion of torrent beds [1]. Altogether, the impact of artificial snow is weaker for very large precipitation events that cause large floods but it is relatively strong for smaller flood events. The impact of artificial snow is 5% for a one year flood event and nearly % for a 1 year flood event. Projected climate change in mountains demonstrate a shift in winter precipitation from snow to rainfall and a change in seasonal precipitation patterns [11], that will change the seasonality of flood events and may well cause more coincidence of the above modelled events and thus stronger environmental impacts. Limits and perspectives of remote sensing Whereas remote sensing techniques are successfully operational for some decades now to monitor the dynamic changes of vast surfaces in the polar regions such as Greenland and Antarctica, remote sensing in mountain environments has basically been applied to monitor changes in glacier extent and snow cover. Apart from the difficulties of resolution, rapidly changing weather conditions still remain a major challenge in mountain remote sensing. Nevertheless, in mountain environments, remote sensing forms a particularly important aspect of data collection, not only to overcome the remoteness of most sites but also to represent the vast variability in small-scale topography and its associated meteorological, hydrological, ecological and anthropogenic characteristics. However, since emerging problems, such as water or territorial conflicts [5], are predominantly small-scale phenomena that occur at the sub-basin scale, suitable remote sensing techniques have to be adapted or developed. Conflicts and human pressures are often local but highly variable over time, such as artificial snow cover over ski runs. Unlike natural snow cover, artificial snow is produced according to man-made decisions inversely to the prevailing weather conditions and unrelated to altitude, thus snow is produced when there is a lack of snow and vice versa. Therefore, derivations of local phenomena from satellite images are not possible. In order to monitor ski runs at the appropriate scale, a grid size resolution of approximately 15 m is required at a high enough temporal resolution to follow rapid changes in snow cover production and snow melt. Daily terrestrial photography from opposite valley slopes coupled with high resolution DEMs [12] offers one of the most appropriate and low-cost techniques to monitor snow cover and estimate snow water equivalent from ski runs. In addition, airborne remote sensing from either small aircrafts or ultralight trikes should be applied periodically over representative ski areas to collect relevant meteorological data such as air humidity as an indicator of evaporation from artificial snow [13] as well as detailed images. Unless too outdated, aerial photography and google earth or IGN images (Fig. 5) provide a precise method for field orientation and establishment of status quo of ski resort development but are inadequate for continual monitoring or to establish the pre-development stage.

7 For example, one problem associated with snow water reservoirs for artificial snow production is the lack of high resolution images showing the extent of wetlands or lakes that existed prior to their construction. These reservoirs are increasing rapidly in number. Radar inferometry should provide useful information for monitoring the filling levels of these reservoirs for hydrological monitoring and to establish the thickness of snow cover on the ski runs. Fig. 5 IGN (Institut Geographique National) image of the Arc ski area (with all 6 torrent catchments) in the summer of 21 showing the high density of ski runs and roads. CONCLUSION This study focuses on hydrological modeling of the effects of artificial snow on discharge. Melt from artificial snow has significant impacts on the local water cycle both at the seasonal and diurnal scale. The impacts on discharge can be strong several weeks after natural snow melt during the summer months as well as for high frequency, low magnitude flood events. One of the major problems of hydrological modeling at the sub-basin level is the lack of meteorological stations in the headwater region and the lack of discharge monitoring of modified streams for calibration purposes. In future, climatic variability is likely to cause rapid changes in the sensitive discharge regimes of mountain torrents and require higher precision in discharge forecasting. This is both to ensure minimal ecological discharge and to avoid water conflicts from developing. Therefore, in addition to the development and operationalisation of stream hydrological models, there is a need to install low-cost, regionally representative hydroclimatological stations above and below all major ski resorts, in particular below the outlet of artificial snow water reservoirs. Remote sensing via oblique terrestrial photography coupled with high precision DEMs should provide the necessary information for both artificial and natural snow cover of ski runs and should also become a routine installation in all major ski resorts. This in turn, can be converted into snow water equivalent and be fed into hydrological models. In addition, quantification of losses from the water cycle by evaporation from artificial snow is important for water management purposes. This can be measured periodically during the snowmaking season via low altitude, instrumented flights.

8 ACKNOWLEDGEMENTS We would like to acknowledge the Societé le Montagnes de l Arc for the availability of data as well as ORODIA, the Service Techniques de Bourg-Saint-Maurice, EDF, CEN and Meteo France for help with GIS, programmes, models and data. This work is based on the Master Thesis of Thierry Barth at the University of Savoy. REFERENCES [1] C. de Jong Artificial snow drains mountain resources EnvironmentalResearchWeb, Talking Point Article. August 27. [2] R. Barry Alpine Climate Change and Cryospheric Responses: An Introduction. Climate and Hydrology in Mountain Areas. (Eds de Jong, C., Collins, D. and Ranzi, R.), J. Wiley and Sons, p. 1-4, 25. [3] C.de Jong., F. Whelan, F., B. Messerli. The importance of a hydrological research framework for water balance studies in high mountain basins : Special Issue Mountain Hydrology in Hydrological Processes, Vol 19:12, p [4] T. Barth Etude de l impact du transfert d eau du basin versant d Arc 2 au basin versant des Arcs 16-18, dans le cadre de l enneigement culture. (Study of the impact of water transfer of the basin of Arc 2 to Arcs in the frame of artificial snow production) Master Thesis Science and Technology. Applied Mountains Sciences, University of Savoy, pp. 286, September 27. [5] C. de Jong Resource conflicts in mountain: source and solutions, Mountain Forum Bulletin, Vol VIII, Isuue 1, p. 5-6., January 28. [6] M. Koscielny Impacts des amenagements en montagne sur l evolution geodynamique des versants. Application au site des Arcs (Savoie, France) (Impacts of installations in mountains on the geodynamics of slope evolution), Doctoral Thesis, Univrsité de Marne la Vallée, Paris. pp [7] N.O. Nascimento Appreciation a l aide d un modele emperique des effets d actions anthropiques sur la relation pluiedebit a l echelle du basin versant (Implementation of an empirical model for investigating the effects of anthropogenic actions on rainfall-runoff relations at the catchment scale, Doctoral Thesis, CERGRENE/ENPC, Paris, pp. 55, [8] N. Edijatno, Nascimento, N.O., Yang, X., Maklhouf, Z., Michel, C. GR3J: a daily watershed model with three free parameters. Hydrological Sciences Journal 44 (2), , [9] Ch. Rixen Ground temperatures under ski pistes with artificial and natural snow, Arctic, Antarctic and AlpineResearch, Vol. 36, No. 4, p , 24. [1] A. Demers Les impacts engendrés par la modification du régime hydrique découlant de l enneigement artificiel (The impacts caused by the modification of the hydrological regime from the discharge of artificial snow) Master Thesis, Centre universitaire de formation en environnement, Université de Sherbrooke, Canada, pp. 74, March 26 [11] Kundzewicz, Z.W., L.J. Mata, N.W. Arnell, P. Döll, B. Jiminez, K. Miller, T. Oki, Z. Sen & I. Shiklomanov The implications of projected climate change for freshwater resources and their management. Hydrological Sciences Journal, 53 (1), February 28. [12] S. Schmidt & B. Weber The Dilemma of Resolution and Seasonality of Snow Cover in Alpine Environments. Proceedings of the Alpine*Snow*Workshop, Munich, 26, Germany. Berchtesgaden National Park Research Report, 53, Berchtesgaden. p , 28. [13] S. Arabas, P. Paccard, L. Haga, W. Junkermann, B. Kulawik, C.de Jong,. Signatures of Evaporation of Artificial Snow in the Alpine Lower Troposphere (SEASALT), EGU abstract 112, April 28.