permafrost distribution modelling in the mountains of the ......permafrost distribution modelling in...

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/228548101

Permafrost distribution modelling in the mountains of the Mediterranean:

Corral del Veleta, Sierra Nevada, Spain

Article in Norsk Geografisk Tidsskrift · November 2010

DOI: 10.1080/00291950152746612

CITATIONS

29READS

106

11 authors, including:

Some of the authors of this publication are also working on these related projects:

State, changes and impact of glacier surface albedo in the Swiss Alps View project

Permafrost and Climate Change in the Antarctic Peninsula - PERMANTAR View project

Luis M. Tanarro

Complutense University of Madrid

53 PUBLICATIONS 410 CITATIONS

SEE PROFILE

Martin Hoelzle

Université de Fribourg

225 PUBLICATIONS 9,956 CITATIONS

SEE PROFILE

Miguel Ramos

University of Alcalá


SEE PROFILE

Stephan Gruber

Carleton University


SEE PROFILE

All content following this page was uploaded by Martin Hoelzle on 01 June 2014.

The user has requested enhancement of the downloaded file.

https://www.researchgate.net/publication/228548101_Permafrost_distribution_modelling_in_the_mountains_of_the_Mediterranean_Corral_del_Veleta_Sierra_Nevada_Spain?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_2&_esc=publicationCoverPdfhttps://www.researchgate.net/publication/228548101_Permafrost_distribution_modelling_in_the_mountains_of_the_Mediterranean_Corral_del_Veleta_Sierra_Nevada_Spain?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_3&_esc=publicationCoverPdfhttps://www.researchgate.net/project/State-changes-and-impact-of-glacier-surface-albedo-in-the-Swiss-Alps?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_9&_esc=publicationCoverPdfhttps://www.researchgate.net/project/Permafrost-and-Climate-Change-in-the-Antarctic-Peninsula-PERMANTAR?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_9&_esc=publicationCoverPdfhttps://www.researchgate.net/?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_1&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Luis-Tanarro-2?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_4&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Luis-Tanarro-2?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_5&_esc=publicationCoverPdfhttps://www.researchgate.net/institution/Complutense_University_of_Madrid?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_6&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Luis-Tanarro-2?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_7&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Martin-Hoelzle?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_4&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Martin-Hoelzle?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_5&_esc=publicationCoverPdfhttps://www.researchgate.net/institution/Universite-de-Fribourg?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_6&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Martin-Hoelzle?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_7&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Miguel-Ramos-14?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_4&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Miguel-Ramos-14?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_5&_esc=publicationCoverPdfhttps://www.researchgate.net/institution/University-of-Alcala?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_6&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Miguel-Ramos-14?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_7&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Stephan-Gruber?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_4&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Stephan-Gruber?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_5&_esc=publicationCoverPdfhttps://www.researchgate.net/institution/Carleton_University?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_6&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Stephan-Gruber?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_7&_esc=publicationCoverPdfhttps://www.researchgate.net/profile/Martin-Hoelzle?enrichId=rgreq-d0250bd23d5f4a6441a44e593bb4990a-XXX&enrichSource=Y292ZXJQYWdlOzIyODU0ODEwMTtBUzoxMDMzMTY3ODA0ODY2NjNAMTQwMTY0NDA0ODI0OA%3D%3D&el=1_x_10&_esc=publicationCoverPdf

Permafrost distribution modelling in the mountains of the Mediterranean:Corral del Veleta, Sierra Nevada, Spain

LUIS MIGUEL TANARRO, MARTIN HOELZLE, ANTONIA GARCÍA, MIGUEL RAMOS, STEPHAN GRUBER, ANTONIO GÓMEZ,MONTSERRAT PIQUER & DAVID PALACIOS

Tanarro, L. M., Hoelzle, M., Garcõ´a, A., Ramos, M., Gruber, S., Gómez, A., Piquer, M. & Palacios, D. 2001. Permafrost distribu-tion modelling in the mountains of the Mediterranean: Corral del Veleta, Sierra Nevada, Spain. Norsk Geogra sk Tidsskrift–Norwegian Journal of Geography Vol. 55, 253–260. Oslo. ISSN 0029-1951.

A statistical model for automated mapping of the spatial distribution of permafrost in the area of Corral del Veleta in south-eastSpain (37°03’ N, 3°22’ W; 3398 m a.s.l.) was developed and applied. The model uses a relationship between permafrost occurrenceas indicated by BTS measurements, and variables such as altitude, solar radiation and summer snow cover. The model was imple-mented within a geographical information system (GIS) and determines the spatial distribution of probable permafrost in Corral delVeleta. Validation was achieved by comparing the predicted permafrost distribution with the results of recent eldwork, such asgeomorphic mapping, geophysical soundings and ground temperature logging.

Keywords: BTS, DTM, GIS, permafrost distribution model, Sierra Nevada, statistical analysis

Luis Miguel Tanarro, David Palacios, Department of A.G.R. and Physical Geography, Complutense University, Ciudad Univer-sitaria s/n, 28040 Madrid, Spain. E-mail: [email protected], [email protected]. Martin Hoelzle, Stephan Gruber, Departmentof Geography, Glaciology and Geomorphodynamics Group, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland. E-mail:[email protected], [email protected]. Antonia Garcṍ a, Antonio Gómez, Montserrat Piquer, Servei de Gestió i Evoluciódel Paisatge. Universitat de Barcelona, C/Baldiri Reixac, s/n. 08028 Barcelona, Spain. E-mail: [email protected],[email protected], [email protected]. Miguel Ramos, Department of Physics, Alcalá de Henares University,E-28871 Alcalá de Henares, Madrid, Spain. E-mail: [email protected].

IntroductionAn important line of mountain-permafrost research is thedevelopment and use of automated mapping methods fordetermining the spatial distribution of permafrost and itsboundaries. The derived information or maps are useful inland management and natural disaster surveillance (Haeberli1992, King et al. 1992). Several numerical models thatpredict the distribution of permafrost in the mountainousregions of central and northern Europe have been developedin recent years, particularly in the Swiss Alps (Hoelzle 1996,Ødegaºrd et al. 1996, 1999, King 2000, Etzelmüller et al.1998, 2001, Hoelzle et al. 2001).

Little is known, however, about the spatial distribution ofpermafrost in the Mediterranean mountains, although geo-physical prospecting, shallow probing and other techniques(Gómez et al. 1999, 2001) indicate that there is permafrost inthese areas, albeit limited to a few high-altitude sites. Sincetoday’s climatic conditions are unfavorable for the existenceof permafrost at these sites, it is assumed to be in a markedstate of degradation. Such conditions make the mountainareas of the Mediterranean interesting for investigatingpermafrost occurrence and its spatial change in view ofatmospheric warming.

This study focuses on the development of a predictivemodel of permafrost distribution at a small test site(2.76 km2 ) located in Corral del Veleta, Sierra Nevada insouth-east Spain. The model is based on the relationshipbetween permafrost occurrence as indicated by BTSmeasurements, and variables such as altitude, potential directsolar radiation (daily mean from July to October) andsummer snow cover.

The relationships between these variables at each of theBTS points were determined by statistical analysis, espe-cially multiple linear regression. With the obtained relation-ships, the supposed permafrost distribution could becalculated in a GIS for the area of the Corral del Veleta,using elevation, radiation and summer snow cover asindependent variables.

Models for automated mapping of the spatialdistribution of mountain permafrostEarly attempts to predict permafrost occurrence resulted inthe so-called ‘rules of thumb’ which evolved from studiesconducted in the Swiss Alps in the mid-1970s (Haeberli1973, 1975). These studies were based on empiricalobservation and the systematic study of a set of variablesthat included:

* geophysical characteristics (seismic refraction and geo-electric resistivity)

* thermal indicators (temperature of the active layer,temperature of springs in autumn, or bottom temperatureof winter snow – BTS)

* geomorphic features (e.g. active rock glaciers)* vegetation cover* topography (altitude, aspect and slope)* snow (accumulation and redistribution of snow by wind

and avalanches).

Starting in the 1990s, predictive models based on DTMswere developed in a GIS environment for automated

Norsk geogr. Tidsskr. Vol. 55, 253–260. Oslo. ISSN 0029-1951

mapping of permafrost distribution (Keller 1992, Hoelzle &Haeberli 1995, Hoelzle 1992, 1996, Imhof 1996, Ødegaºrd etal. 1996, Etzelmüller et al. 1998, Frauenfelder et al. 1998,Gruber & Hoelzle 2001).

These predictive models can be grouped according to thevariables and methods used (Hoelzle et al. 2001). Earlymodels such as PERMAKART (Keller 1992) and PERM(Imhof 1996) were GIS applications based on a fewempirical rules including topographic factors such as slope,aspect and altitude.

More recent models use topo-climatic, biogeographic andgeomorphic information (e.g. rock glaciers and perennialsnow patches), and thermal characteristics (e.g. springtemperatures and BTS) (Frauenfelder et al. 1998).

A second group of applications consisting of morephysically-based statistical models use statistical analysisto determine the spatial relationship between BTS measure-ments and topo-climatic variables. PERMAMAP establishessuch a relationship between BTS measurements, and meanannual air temperature and potential direct solar radiation(Hoelzle 1992, 1996, Funk & Hoelzle 1992, Hoelzle et al.1993, Hoelzle & Haeberli 1995). Other models are based onsimilar criteria using statistical relations between BTS andaltitude, snow depth and remote-sensing data (apparentsatellite temperature and vegetation index) (Ødegaºrd et al.1999), or the relationship between BTS measurements, solarradiation and altitude, and have the possibility of includingbiogeographic indicators (vegetation index). As an alter-native to BTS measurements, this approach can also becalibrated using geomorphic features such as an inventory ofrock glaciers (Gruber & Hoelzle 2001).

New developments in physical process models likePERMEBAL (energy balance and thermal offset models)(Mittaz et al. 2000, Hoelzle et al. 2001) take into account themost important physical factors associated with the energybalance.

The model developed in this study pertains to the group ofphysically-based statistical models that use statistical analy-sis to predict the spatial distribution of permafrost andfollows largely the strategy outlined in Gruber & Hoelzle(2001).

Study siteThe study was conducted in the area of Corral del Veleta,located on the far western end of the Sierra Nevada Massif(37°03’24@N, 3°22’05@W) in the Cryo-Mediterranean biocli-mate zone. The massif is part of the Béticas Range in south-eastern Spain and includes some of the highest peaks on theIberian Peninsula (Mulhacén 3483 m a.s.l., Veleta 3398 ma.s.l., and Alcazaba 3371 m a.s.l.) (Fig. 1).

The small study site at Corral del Veleta (2.76 km2 ) islocated in a cirque, in an area glaciated during theQuaternary. The site faces N–NE, and is bordered by anabrupt, rock headwall with crags as high as 300 m. Thesummit of Pico del Veleta rises above the wall to an altitudeof 3398 m a.s.l. The headwall is composed of deformedfeldspathic mica schists that are now exposed to gelifraction.A structured rock ledge appears at an altitude between

3050 m a.s.l. and 3150 m a.s.l. and forms the base of thecirque. The surface of the ledge is covered with terminal andlateral morainic deposits which accumulated during theperiod between the Late Glacial and the Little Ice Age.Detrital deposits from periglacial and nival morphogenesisappear between the morainic materials and the base of thewall. The most salient features of these deposits are therockfall taluses, rock avalanches, dead-ice depressions,polygonal soils, pronival ramparts, and ice-creep formations.There is also evidence of perennial ice slabs and perennialsnow patches (Gómez et al. 1996, 1999).

Modelling parameters

Bottom temperature of winter snow (BTS)

The bottom temperature of winter snow cover is a goodindicator of the presence of mountain permafrost (Haeberli &Patzelt 1982). The BTS method was originally developed byHaeberli (1973), and has been used extensively in manymountain areas (Hoelzle 1992, 1996, Guglielmin & Tellini1993, Hoelzle at al. 1993, 1994, Gardaz 1997, King &Kalisch 1998, Imhof et al. 2000). Since snow is an excellentinsulator, it will shield the ground surface from airtemperature variations if it remains stable and at least80 cm thick throughout the winter. Under conditions thatare controlled by the ground heat ux, the BTS will remainfairly constant in late winter. BTS values are grouped intothree categories (Haeberli 1973): (a) probable permafrost(BTS = ¡2°C).

A total of 121 BTS values were measured in 1999 and2000, during the month of March. Unfortunately, it wasimpossible to obtain more complete BTS data, since roughterrain and bad weather made it dif cult to access the eldsites, and at times the snow cover was too thin. Attempts toobtain readings from the west-facing wall near the summit ofPico del Veleta between altitudes of 3300 m a.s.l. and3398 m a.s.l. were more successful. Data were also collected

Fig. 1. Location of study area indicating the main peaks of the Sierra NevadaMassif.

254 L. M. Tanarro et al. NORSK GEOGRAFISK TIDSSKRIFT 55 (2001)

at the cirque, particularly in the eastern sector betweenaltitudes of 3070 m and 3150 m and at sites with N, NE andNW aspects (Fig. 2 and Table 1). Temperatures below ¡3°Cwere more frequent at the summit area near the rock cliff, andbetween the base of the rock wall and the upper reaches ofthe rockfall talus in the eastern sector of the cirque than atother test sites.

Altitude

Altitude strongly in uences air temperature in mountainousregions, and can be used as a parameter in modellingpermafrost distribution.

A Digital Terrain Model (DTM) was created by digitizingstandard topographical maps at a scale of 1:10,000 (Fig. 3). ATriangulated Irregular Network (TIN) was used for inter-

polating elevation values based on digitized contour lines,breaklines and x-points. Then a lattice of 348 columns and318 rows with a resolution of 5 m was generated.

Potential solar radiation

Solar radiation during snow-free conditions is a major factorin determining permafrost distribution in mountain areas atlower latitudes, and exerts a strong in uence on surfacetemperature and the energy balance. Latitude, cloud coverand haziness are conditions that generally affect solarradiation, but in mountainous areas shading by surroundingterrain plays an important role. The slope angle, aspect andtopographic shading directly affect the amount of solarradiation received by the ground surface (Funk & Hoelzle1992, Hoelzle et al. 1993).

SRAD, a spatially-distributed radiation balance modeldeveloped by Moore et al. (1993), as part of the ‘Terrainanalysis programs for environmental sciences’ – TAPES-G –(Gallant & Wilson 1996, Wilson & Gallant 2000, discussedin Heggem et al. in press), was used to calculate the potentialsolar radiation.

The calculated SRAD radiation values represent meandaily potential direct solar radiation (MJ/m2d) for July toOctober, when snow cover is minimal and radiation highest.On the high, north-facing rock cliff where shading isdominant, modelled radiation was very low (Fig. 4).

Summer snow cover

Snow cover alters the energy balance at the surface byreducing strongly the direct solar radiation, and therefore theground surface below the snow patches cannot be warmerthan 0°C (Hoelzle 1992, Keller 1992). There is littleoccurrence of perennial snow patches in the Mediterraneanmountains, since summer temperatures are high. However,some snow patches can persist at high altitudes andtopographically favourable places, and they are goodindicators of permafrost. The base of Corral del Veleta isone of the areas in Sierra Nevada where large snow patchesnormally survive the summer. Since 1995 (with the excep-tion of 1998), however, snow cover almost completelydisappears within the year.

Table 1. Distribution of BTS measurements according to permafrost classes.

BTS temperatures BTS points Total Permafrost classes

¡6°C – ¡7°C 2 36 Permafrost probable¡5°C – ¡6°C 7¡4°C – ¡5°C 14¡3°C – ¡4°C 13¡2°C – ¡3°C 39 39 Permafrost possible¡1°C – ¡2°C 29 46 No permafrost0°C – ¡1°C 17Total 121 121

Fig. 2. Distribution of the BTS data according to altitude, aspect, slope, snowthickness and temperature.

Fig. 3. Digital Terrain Model showing the topographic characteristics of theCorral del Veleta area.

NORSK GEOGRAFISK TIDSSKRIFT 55 (2001) Permafrost distribution modelling in the mountains of the Mediterranean 255

Summer snow cover mapping was achieved by interpret-ing three aerial photographs taken in July 1957, 1985 and1999 (scale 1:33,000, 1:30,000 and 1:25,000, respectively).An orthophoto (September 1989, scale 1:5,000) was used asan additional snow map and served as base map for the otheraerial photographs.

The analysis of the orthophoto based on the three ightsresulted in four polygon maps containing information aboutthe areas with presence or absence of snow. These maps wereconverted to raster format and were reclassi ed: a value of 0

was given to the locations without snow and a value of 1 tothose with snow. The processing of these maps was done byaddition of all four maps to a single map having values from0 to 4. The legend delineates locations of similar frequenciesof having a summer snow cover, based on the existingevidence. Areas without snow have a cumulative value of 0,areas that have a summer snow cover on all photographshave a value of 4 (Fig. 5).

Statistical analysisAttribute values for each BTS point were determined fromgrid maps (5 m £ 5 m resolution) designed for individualvariables. This information was subsequently used tocorrelate BTS with altitude, solar radiation and summersnow cover values. A synthesis of the spatial distribution ofBTS classes in relation to each parameter is given in Table 2.

Linear correlation

The analysis yielded correlation coef cients (Table 3) thatindicate a good relationship between the following sets ofvariables: altitude and summer snow cover, altitude andradiation, and radiation and summer snow cover. In contrast,the overall relationship between these variables and BTS was

Fig. 4. Mean daily potential solar radiation in the Corral del Veleta for themonths July to October, calculated with SRAD program. LEGEND: 1:30 MJ/m2/day; 8:BTS = ¡2°C; 11: Morainiccrest; 12: Rockwall scarps; 13: Lakes; 14: Fluvial network; 15: Road; 16: Ski-lift.

Fig. 5. Summer snow cover remains in the Corral del Veleta area, utilizingseveral collections of aerial photographs. LEGEND: 1: No snow cover insummer; 2: Low summer snow cover remain; 3: Medium summer snow coverremain; 4: High summer snow cover remain; 5: Maximum summer snowcover remain; 6: BTS = ¡2°C;9: Morainic crest; 10: Rockwall scarps; 11: Lakes; 12: Fluvial network; 13:Road; 14: Ski-lift.

Table 2. Spatial distribution of BTS points in relation to altitude, exposition,potential solar radiation and summer snow cover remain.

BTS points >¡3°C ¡2°C – ¡3°C

not as good. For example, the correlation between altitude (apivotal factor in controlling air temperature) and BTS ispoor, suggesting that altitude is only a secondary factor inpermafrost distribution in this area. The altitude range of theinvestigated area is rather small, which may explain the poorcorrelation. Permafrost can occur, in fact, at very lowaltitudes when speci c topographic conditions and lowradiation exist (e.g. inside the cirque at Corral del Veleta).In the Alps, permafrost has been detected at very low altitude(1800 m and lower) in areas where mean annual tempera-tures are above 0°C (Hoelzle 1992, 1996, Hoelzle & Haeberli1995).

The correlation between BTS measurements and solarradiation was relatively good, so it can be concluded thatradiation, strongly in uenced by aspect, slope and topo-graphic shading, is the most important factor in determiningpermafrost occurrence on the headwall of the cirque and onthe mid-upper regions of the rockfall talus where modelledpotential direct solar radiation is below 10MJ/m2d.

Multiple regression analysis

The nal step in the development of the permafrostdistribution model involved two multiple regression analysesthat compared two sets of the variables linked to BTS. Thepurpose of these analyses was to determine if the addition ofthe summer snow cover factor would alter the model. Oneanalysis was based on the altitude and radiation variables,while the other included these and the summer snow coverfactor.

In the rst scenario, the analysis produced a correlationcoef cient of r2 = 0.193 based on the following function:

BTS = 14.983 – 0.0063 * Altitude ‡ 0.125 * RadiationThe coef cient obtained from the second analysis was

r2 = 0.196, and the function was:

Table 3. Linear correlation coefficients between parameters used formultiple regression analysis and BTS measurements.

Altitude r = ¡0.1247r2 = 0.0155

Radiation r = 0.2582 r = 0.6093r2 = 0.0667 r2 = 0.3713

Summer snow cover r = 0.0432 r = ¡0.8130 r = ¡0.6586r2 = 0.0019 r2 = 0.661 r2 = 0.4338

BTS Altitude Radiation

Fig. 6. Permafrost distribution modelled in the Corral del Veleta area using a statistical relation between BTS, altitude and potential solar radiation.


BTS = 11.789 – 0.0054 * Altitude ‡ 0.131 * Radiation ‡0.095 * Summer snow cover

The functions were implemented in a GIS application toproduce two maps of the spatial distribution of permafrost atCorral del Veleta. One map incorporated altitude andpotential solar radiation variables (Fig. 6), while the otherincluded these and the summer snow cover factor (Fig. 7).

Discussion of the permafrost models

In the rst model, the multiple regression analysis betweenBTS and independent variables altitude and potential solarradiation explains 19.3% of the variability of the BTSmeasurements. The standard deviation of the residuals is1.323. The linear regression shows that potential solarradiation is the parameter with the best correlation. However,it is not very high, since it can only explain 7% of thevariance in BTS.

Altitude is not signi cant, explaining only 1.5% of thevariance in BTS. Nevertheless, altitude has an importantcontribution when it is incorporated together with thepotential solar radiation in the multiple regression model.The small altitudinal range (less than 350 m), where the BTS

are measured, can explain most of the low correlation (cf.Hoelzle et al. 1993).

The addition of summer snow cover has scarcely improvedthe variance of the BTS (19.6%). The standard deviation ofthe residuals is 1.326. In addition, summer snow cover isstatistically not signi cant in the model (p-value = 0.527higher than 0.10 at the 90% con dence level). However,summer snow cover is strongly correlated with altitude(r = 0.81), and even with potential solar radiation(r = ¡0.66). The two independent variables, altitude andsummer snow cover, with their strong correlation prevent animprovement of the explained variability by the summersnow cover.

Model validationModel validation was done by detailed geomorphic mapping,geophysical prospecting and continuous ground temperaturelogging (Gómez et al. 2001).

Geomorphic eld observations con rmed the existence offormations related to active processes in the upper section ofthe rockfall talus located on the far eastern side of the cirque,where the model has simulated probable permafrost. Theseactive processes, including geli uction lobes, mud ows,landslide scars and ice-creep features, are partly related to

Fig. 7. Permafrost distribution modelled in the Corral del Veleta area, using a statistical relation between BTS, altitude, potential solar radiation, and summersnow cover.


recent permafrost degradation and intense snow melt(Gómez et al. 1999, 2001, Luengo et al. 2000, Palacios etal. 2000 ). In addition, increasing rockfall activity from thehigh cliff may indicate permafrost degradation. A rockglacier-like feature indicating creeping permafrost is presentin the cirque, where ice was detected by shallow borehole at adepth of 1.5 m. Thermal probes (Tinytalk-II) were used toautomatically log continuous ground temperature in thisborehole. Mean temperatures below ¡5°C were recordedduring the winter months at the front of the rock glacier(Ramos et al. 2000).

High-resolution refraction-seismic and DC resistivitytomography sounding performed at various pro les on the oor of Corral del Veleta indicated interstitial ice orpermafrost in the eastern sector. Resistivity values from50,000 to 200,000 «m were obtained in creeping permafrostand increased to 562,000 «m in the direction of the rockfalltalus, near the base of the rock cliff. Seismic velocity valuesfor this area were also very high (>3600 m/s) (Gómez et al.1999, 2001).

Veri cation with these eld measurements and mapsshows a good coincidence with the modelled permafrostdistribution. Therefore, it can be assumed that the modelgives a realistic picture of today’s permafrost distribution inthis area.

Conclusions and perspectivesThe permafrost distribution model developed in this studyrepresents an initial attempt to use automated mapping of thespatial distribution of permafrost in a mountainous area ofthe Mediterranean. The relative importance of the parametersthat govern permafrost occurrence in this region is differentfrom that in the mountains of northern and central Europe.

Statistical analyses like the ones presented here are ideallybased on representative and independent data. Dif cultaccess and conditions in the eld in uence strongly themeasured sample of BTS values. The statistical resultsshould therefore be considered mainly in view of our processunderstanding, which has strongly in uenced our selection ofindependent variables. These variables are indeed notindependent. This means that basic assumptions of statisticalanalysis are not completely ful lled.

However, the statistical relations between the modellingparameters are weak, but the analysis of variance has showna statistically signi cant relationship between the variables.The rst model, including altitude and potential solarradiation, offers a useful relation. The second approach,with an additional incorporation of summer snow cover as athird variable, has not improved the model signi cantly.

From the statistics, potential solar radiation seems to bethe variable with the most in uence in determining thepermafrost distribution in the investigated area. Altitude hasminor importance, mainly due to the small altitude range.

The result of the spatial permafrost distribution model hasallowed estimation of the presence of permafrost in theCorral del Veleta area, which is mainly in uenced by thespecial topographic setting favouring low solar radiation andthe existence of perennial snow patches.

The model is veri ed by eld evidence, such asgeomorphic indicators, geophysical soundings and geother-mal monitoring. The applied veri cation methods in themodelled permafrost area revealed buried ice, con rming thepresence of permafrost.

For the future, further effort is needed to obtain a morecomplete set of BTS measurements at carefully selectedsites, as this would ensure greater model accuracy (especiallyin order to understand better the relation between BTS andsummer snow cover). Installation of some mini-temperaturedata loggers in the rock cliff would help with estimation ofthe mean annual surface temperatures.

Acknowledgements.–This model was developed at the University of Zurich–Irchel, Department of Geography, and the authors’ participation in the studywas made possible by funding from the European PACE Project (ContractENV4-CT97-0492) and Spanish National Projects (DGICYT contract PB96-0385 and BSO 2000-0745). We thank Christian Hauck, Enrique Luengo,Fernando Crespo and Jorge Vicens for their assistance in obtaining BTSmeasurements during winter eldwork. Appreciation also goes to CETURSAS.A., proprietors of the ski resort in Sierra Nevada, and to Sierra NevadaNational Park, for their help in providing facilities and logistic support. Wealso thank Alice Ferrero for her excellent translation of the text into English.We greatly acknowledge very constructive and helpful comments from RuneØdegaºrd.

Manuscript submitted 1 May 2001; accepted 1 August 2001

References

Etzelmüller, B., Berthling, I. & Sollid, J. L. 1998. The distribution ofpermafrost in southern Norway – a GIS approach. Lewkovicz, A. G. &Allard, M. (eds.) 7th International Conference on Permafrost, (Yellow-knife, 23–27 June 1998), Collection Nordicana 57, 251–256. Centred’études nordiques, Université Laval, Québec.

Etzelmüller, B., Ødegaºrd, R., Berthling, I. & Sollid, J. L. 2001. Terrainparameters and remote sensing in the analysis of permafrost distributionand periglacial processes: principles and examples from southern Norway.Permafrost and Periglacial Processes 12, 79–92.

Frauenfelder, R., Allgöwer, B., Haeberli, W. & Hoelzle, M. 1998. Permafrostinvestigations with a GIS – a case study in the Fletschhorn area, Wallis,Swiss Alps. Lewkovicz, A. G. & Allard, M. (eds.) 7th InternationalConference on Permafrost, (Yellowknife, 23–27 June 1998), CollectionNordicana 57, 291–296. Centre d’études nordiques, Université Laval,Québec.

Funk, M. & Hoelzle, M. 1992. A model of potential direct solar radiation forinvestigating occurrences of mountain permafrost. Permafrost andPeriglacial Processes 3, 139–142.

Gallant, J. C. & Wilson, J. P. 1996. TAPES-G: a grid-based terrain analysisprogram for the environmental sciences. Computer and Geosciences 22,713–722.

Gardaz, J. M. 1997. Distribution of mountain permafrost, Fontanesses Basin,Valaisian Alps, Switzerland. Permafrost and Periglacial Processes 8,101–105.

Gómez, A., Schulte, L. & Salvador, F. 1996. Contribució n al conocimientode la deglaciació n reciente y morfolog õ´a asociada del Corral del Veleta(Sierra Nevada). Cadernos do Laboratorio Xeológico de Laxe 21, 543–558.

Gómez, A., Palacios, D., Ramos, M., Schulte L., Salvador, F. & Tanarro, L.M. 1999. Degradación de permafrost en Sierra Nevada y repercusionesgeomorfológicas: El caso del Corral del Veleta. Resultados preliminares.Bolet ṍ n de la Asociación de Geógrafos Españoles 27, 7–21.

Gómez, A., Palacios, D., Ramos, M., Tanarro, L. M., Schulte, L. & Salvador,F. 2001. Location of permafrost in marginal regions: Corral del Veleta,Sierra Nevada, Spain. Permafrost and Periglacial Processes 12, 93–110.

Gruber, S. & Hoelzle, M. 2001. Statistical modelling of mountain permafrost


http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^2912L.79[aid=1888147]http://www.ingentaconnect.com/content/external-references?article=/0098-3004^28^2922L.713[aid=1888149]http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^298L.101[aid=1888150]http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^2912L.93[aid=1888151]http://www.ingentaconnect.com/content/external-references?article=/0098-3004^28^2922L.713[aid=1888149]http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^298L.101[aid=1888150]

distribution: local calibration and incorporation of remotely sensed data.Permafrost and Periglacial Processes 12, 69–77.

Guglielmin, M. & Tellini, C. 1993. First example of permafrost mapping withBTS in the Italian Alps, Livigno, Sondrio, Italy. Acta Naturalia 29, 39–46.

Guglielmin, M. & Tellini, C. 1994. Rilevamento del permafrost con ilmetodo BTS (Bottom Temperature of the Winter Snow Cover) nell’altaValtellina (Sondrio, Italia). Geogra a Fisica e Dinamica Quaternaria 17,47–54.

Haeberli, W. 1973. Die Basis temperatur der winterlichen Schneedecke alsmöglicher Indikator für die Verbreitung von Permafrost in den Alpen.Zeitschrift für Gletscherkunde und Glazialgeologie 9, 221–227.

Haeberli, W. 1975. Untersuchungen zur Verbreitung von Permafrostzwischen Flüelapass und Piz Grialetsch (Graubünden). Mitteilungen derVersuchsanstalt für Wasserbau, Hydrologie und Glaziologie ETH Zürich17. 221 pp.

Haeberli, W. 1992. Construction, enviromental problems and natural hazardsin periglacial mountain belts. Permafrost and Periglacial Processes 3,111–124.

Haeberli, W. & Patzelt, G. 1982. Permafrostkartierung im Gebiet derHochebenkar-Blockgletscher, Obergurgl, Ötztaler Alpen. Zeitschrift fürGletscherkunde und Glazialgeologie 17, 127–150.

Heggem, E. S. F., Etzelmüller, B. & Berthling, I. 2001. Topographicradiation balance models – sensitivity and application in periglacialgeomorphology. Norsk Geogra sk Tidsskrift – Norwegian Journal ofGeography 55, 203–211.

Hoelzle, M. 1992. Permafrost occurrence from BTS measurements andclimatic parameters in the Eastern Swiss Alps. Permafrost and PeriglacialProcesses 3, 143–147.

Hoelzle, M. 1996. Mapping and modelling of mountain permafrostdistribution in the Alps. Norsk Geogra sk Tidsskrift 50, 11–15.

Hoelzle, M. & Haeberli, W. 1995. Simulating the effects of mean annual air-temperature changes on permafrost distribution and glacier size: anexample from Upper Engadin, Swiss Alps. Annals of Glaciology 21, 399–405.

Hoelzle, M., Haeberli, W. & Keller, F. 1993. Application of BTS-measure-ments for modelling mountain permafrost distribution. 6th InternationalConference on Permafrost, Proceedings , 272–277. South China UniversityTechnology Press, Beijing.

Hoelzle, M., Mittaz, C., Etzelmüller, B. & Haeberli, W. 2001. Surface energy uxes and distribution models of permafrost in European Mountain areas:an overview of current developments. Permafrost and PeriglacialProcesses 12, 53–68.

Imhof, M. 1996. Modelling and veri cation of the permafrost in the BerneseAlps, Western Switzerland. Permafrost and Periglacial Processes 7, 267–280.

Imhof, M., Pierrehumbert, G., Haeberli, W. & Kienholz, H. 2000. Permafrostinvestigations in the Schithorn massif, Bernese Alps, Switzerland.Permafrost and Periglacial Processes 11, 189–206.

Keller, F. 1992. Automated mapping of mountain permafrost using theprogram PERMAKART within the geographical information systemARC/INFO. Permafrost and Periglacial Processes 3, 133–138.

King, L. 2000. Mountain permafrost in Europe: occurrence, characteristics,prospecting, mapping and monitoring. Peña, J. L., Sánchez, M. & Lozano,M. V. (eds) Procesos y Formas Periglaciares en la Montaña Mediterrá-nea, 3–24. Instituto de Estudios Turolenses, Teruel.

King, L. & Kalisch, A. 1998. Permafrost distribution and implications forconstruction work in the Zermatt. Area, Swiss Alps. Lewkovicz, A. G. &Allard, M. (eds.) 7th International Conference on Permafrost (Yellowknife,23–27 June 1998) , Collection Nordicana 57, 569–574. Centre d’étudesnordiques, Université Laval, Québec.

King, L., Gorbunov, A. P. & Evin, M. 1992. Prospecting and mapping ofmountain permafrost and associated phenomena. Permafrost and Perigla-cial Processes 3, 73–81.

Luengo, E., Gómez, A., Tanarro, L. M., Palacios, D., Schulte, L. & Ramos,M. 2000. Inestabilidad de los taludes en alta montaña causada por lapresencia de permafrost: el caso del Corral del Veleta, Sierra Nevada. 6thReunion Nacional de Geomorfolog ṍ a, Abstracts, (Madrid, 17–20 Septem-ber 2000).

Mittaz, C., Hoelzle, M. & Haeberli, W. 2000. First results and interpretationof energy- ux measurements of Alpine permafrost. Annals of Glaciology31, 275–280.

Mittaz, C., Hoelzle, M. & Haeberli, W. 2001. PERMEBAL: A mountainpermafrost distribution model based on energy balance calculations.Abstracts of 1st European Permafrost Conference (Rome, 26–28 March2001), p. 67.

Moore, I. D., Norton, T. W. & Williams, J. E. 1993 Modeling environmentalheterogeneity in forested landscapes. Journal of Hydrology 150, 717–747.

Ødegaºrd, R. S., Hoelzle, M., Johansen, K. V. & Sollid, J. L. 1996. Permafrostmapping and prospecting in southern Norway. Norsk Geogra sk Tidsskrift50, 41–54.

Ødegaºrd, R. S., Isaksen, K., Mastervik, M., Billdal, L., Engler, M. & Sollid, J.L. 1999. Comparison of BTS and Landsat TM data from Jotunheimen,southern Norway. Norsk Geogra sk Tidsskrift 53, 226–233.

Palacios, D., Gómez, A., Fernández, A., Zamorano, J. J., Tanarro, L. M.,Salvador, F., Schulte, L. & Ramos, M. 2000. La formación de debris owsen el Corral del Veleta (Sierra Nevada, España). 6th Reunion Nacional deGeomorfolog ṍ a, Abstracts (Madrid, 17–20 September 2000).

Ramos, M., Gómez, A., Palacios, D., Lóber, A., Salvador, F., Tanarro, L. M.,Schulte, L., Crespo, F. & Luengo, E. 2000. Monitorizació n de ladistribución del permafrost en Sierra Nevada a través del registrosistemático anual de la temperatura del suelo: Primeros resultados. 6thReunion Nacional de Geomorfolog ṍ a, Abstracts (Madrid, 17–20 Septem-ber 2000).

Wilson, J. P. & Gallant, J. C. (eds). 2000. Terrain Analysis: Principles andApplications . John Wiley and Sons, Chichester. 512 pp.


View publication statsView publication stats
http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^297L.267[aid=1888161]http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^2912L.69[aid=1888152]http://www.ingentaconnect.com/content/external-references?article=/0029-1951^28^2950L.11[aid=1888159]http://www.ingentaconnect.com/content/external-references?article=/0260-3055^28^2921L.399[aid=1888160]http://www.ingentaconnect.com/content/external-references?article=/1045-6740^28^297L.267[aid=1888161]http://www.ingentaconnect.com/content/external-references?article=/0260-3055^28^2931L.275[aid=1888164]http://www.ingentaconnect.com/content/external-references?article=/0022-1694^28^29150L.717[aid=787350]http://www.ingentaconnect.com/content/external-references?article=/0260-3055^28^2931L.275[aid=1888164]https://www.researchgate.net/publication/228548101

permafrost distribution modelling in the mountains of the ......permafrost distribution modelling in...

Documents