Predictive GIS-based model of rockfall activity in mountain cliffs
Rockfall susceptibility has been analysed in mountain cliffs of the Cantabrian Range, North Spain. The main aim of this analysis has been to build a predictive model of rockfall activity from a low number of environmental and geological variables. The rockfall activity has been quantified in a GIS....
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| Veröffentlicht in: | Natural hazards (Dordrecht) 2003-11, Vol.30 (3), p.341-360 |
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| creator | MARQUINEZ, J MENENDEZ DUARTE, R FARIAS, P JIMENEZ SANCHEZ, M |
| description | Rockfall susceptibility has been analysed in mountain cliffs of the Cantabrian Range, North Spain. The main aim of this analysis has been to build a predictive model of rockfall activity from a low number of environmental and geological variables. The rockfall activity has been quantified in a GIS. The cartographic information used shows the spatial distribution of all the recent talus screes as well as their associated source areas in the rock-slopes. The area relation At/Ar (recent talus scree polygon/source basins) in the rock slopes has been used as the rockfall activity indicator. This relation has been validated in 50 pilot rock-slopes and compared with the relation number of recent rock fragments/source basin, obtained from field work. The environmental factors causing rockfall depend on the rock slope situation, and these are: altitude and sun radiation on the rock cliff. The geological factors considered are: lithology, relative position of the main discontinuities with respect to the topographic surface and two morphologic parameters: the roughness and slope gradient. A logistic regression analysis has been applied to a population of 442 limestone and quartzite rock cliffs. The dependent variable is the rockfall activity indicator, which allows the definition of two classes of rock cliff units: low and high activity. The independent variables are altitude, sun radiation (equinox radiation, summer solstice radiation, winter solstice radiation), slope roughness, slope gradient,anisotropy and lithology. Results suggest that it is possible tobuild a valid cartographic predictive model for rockfall activity in mountain rock cliffs from a limited number of easily obtainable variables. The method is especially applicable in massive rock slopes or in regions with uniform rock mass characteristics.[PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1023/B:NHAZ.0000007170.21649.e1 |
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The main aim of this analysis has been to build a predictive model of rockfall activity from a low number of environmental and geological variables. The rockfall activity has been quantified in a GIS. The cartographic information used shows the spatial distribution of all the recent talus screes as well as their associated source areas in the rock-slopes. The area relation At/Ar (recent talus scree polygon/source basins) in the rock slopes has been used as the rockfall activity indicator. This relation has been validated in 50 pilot rock-slopes and compared with the relation number of recent rock fragments/source basin, obtained from field work. The environmental factors causing rockfall depend on the rock slope situation, and these are: altitude and sun radiation on the rock cliff. The geological factors considered are: lithology, relative position of the main discontinuities with respect to the topographic surface and two morphologic parameters: the roughness and slope gradient. A logistic regression analysis has been applied to a population of 442 limestone and quartzite rock cliffs. The dependent variable is the rockfall activity indicator, which allows the definition of two classes of rock cliff units: low and high activity. The independent variables are altitude, sun radiation (equinox radiation, summer solstice radiation, winter solstice radiation), slope roughness, slope gradient,anisotropy and lithology. Results suggest that it is possible tobuild a valid cartographic predictive model for rockfall activity in mountain rock cliffs from a limited number of easily obtainable variables. The method is especially applicable in massive rock slopes or in regions with uniform rock mass characteristics.[PUBLICATION ABSTRACT]</description><identifier>ISSN: 0921-030X</identifier><identifier>EISSN: 1573-0840</identifier><identifier>DOI: 10.1023/B:NHAZ.0000007170.21649.e1</identifier><language>eng</language><publisher>Dordrecht: Springer</publisher><subject>Altitude ; Anisotropy ; Cliffs ; Earth sciences ; Earth, ocean, space ; Engineering and environment geology. 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The main aim of this analysis has been to build a predictive model of rockfall activity from a low number of environmental and geological variables. The rockfall activity has been quantified in a GIS. The cartographic information used shows the spatial distribution of all the recent talus screes as well as their associated source areas in the rock-slopes. The area relation At/Ar (recent talus scree polygon/source basins) in the rock slopes has been used as the rockfall activity indicator. This relation has been validated in 50 pilot rock-slopes and compared with the relation number of recent rock fragments/source basin, obtained from field work. The environmental factors causing rockfall depend on the rock slope situation, and these are: altitude and sun radiation on the rock cliff. The geological factors considered are: lithology, relative position of the main discontinuities with respect to the topographic surface and two morphologic parameters: the roughness and slope gradient. A logistic regression analysis has been applied to a population of 442 limestone and quartzite rock cliffs. The dependent variable is the rockfall activity indicator, which allows the definition of two classes of rock cliff units: low and high activity. The independent variables are altitude, sun radiation (equinox radiation, summer solstice radiation, winter solstice radiation), slope roughness, slope gradient,anisotropy and lithology. Results suggest that it is possible tobuild a valid cartographic predictive model for rockfall activity in mountain rock cliffs from a limited number of easily obtainable variables. The method is especially applicable in massive rock slopes or in regions with uniform rock mass characteristics.[PUBLICATION ABSTRACT]</description><subject>Altitude</subject><subject>Anisotropy</subject><subject>Cliffs</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Engineering and environment geology. 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Geothermics</topic><topic>Environmental factors</topic><topic>Exact sciences and technology</topic><topic>Limestone</topic><topic>Lithology</topic><topic>Natural hazards: prediction, damages, etc</topic><topic>Prediction models</topic><topic>Regression analysis</topic><topic>Rocks</topic><topic>Solstices</topic><topic>Spatial distribution</topic><topic>Studies</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>MARQUINEZ, J</creatorcontrib><creatorcontrib>MENENDEZ DUARTE, R</creatorcontrib><creatorcontrib>FARIAS, P</creatorcontrib><creatorcontrib>JIMENEZ SANCHEZ, M</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Earthquake Engineering Abstracts</collection><jtitle>Natural hazards (Dordrecht)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>MARQUINEZ, J</au><au>MENENDEZ DUARTE, R</au><au>FARIAS, P</au><au>JIMENEZ SANCHEZ, M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive GIS-based model of rockfall activity in mountain cliffs</atitle><jtitle>Natural hazards (Dordrecht)</jtitle><date>2003-11-01</date><risdate>2003</risdate><volume>30</volume><issue>3</issue><spage>341</spage><epage>360</epage><pages>341-360</pages><issn>0921-030X</issn><eissn>1573-0840</eissn><abstract>Rockfall susceptibility has been analysed in mountain cliffs of the Cantabrian Range, North Spain. The main aim of this analysis has been to build a predictive model of rockfall activity from a low number of environmental and geological variables. The rockfall activity has been quantified in a GIS. The cartographic information used shows the spatial distribution of all the recent talus screes as well as their associated source areas in the rock-slopes. The area relation At/Ar (recent talus scree polygon/source basins) in the rock slopes has been used as the rockfall activity indicator. This relation has been validated in 50 pilot rock-slopes and compared with the relation number of recent rock fragments/source basin, obtained from field work. The environmental factors causing rockfall depend on the rock slope situation, and these are: altitude and sun radiation on the rock cliff. The geological factors considered are: lithology, relative position of the main discontinuities with respect to the topographic surface and two morphologic parameters: the roughness and slope gradient. A logistic regression analysis has been applied to a population of 442 limestone and quartzite rock cliffs. The dependent variable is the rockfall activity indicator, which allows the definition of two classes of rock cliff units: low and high activity. The independent variables are altitude, sun radiation (equinox radiation, summer solstice radiation, winter solstice radiation), slope roughness, slope gradient,anisotropy and lithology. Results suggest that it is possible tobuild a valid cartographic predictive model for rockfall activity in mountain rock cliffs from a limited number of easily obtainable variables. The method is especially applicable in massive rock slopes or in regions with uniform rock mass characteristics.[PUBLICATION ABSTRACT]</abstract><cop>Dordrecht</cop><pub>Springer</pub><doi>10.1023/B:NHAZ.0000007170.21649.e1</doi><tpages>20</tpages></addata></record> |
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| subjects | Altitude Anisotropy Cliffs Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Environmental factors Exact sciences and technology Limestone Lithology Natural hazards: prediction, damages, etc Prediction models Regression analysis Rocks Solstices Spatial distribution Studies Variables |
| title | Predictive GIS-based model of rockfall activity in mountain cliffs |
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