Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China
Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling ut...
Ausführliche Beschreibung
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| Autor*in: |
Hu, Man [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Environmental earth sciences - Springer Berlin Heidelberg, 2009, 83(2024), 6 vom: März |
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| Übergeordnetes Werk: |
volume:83 ; year:2024 ; number:6 ; month:03 |
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| DOI / URN: |
10.1007/s12665-024-11463-y |
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| Katalog-ID: |
SPR055087728 |
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| 520 | |a Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. | ||
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| 650 | 4 | |a Inclinometers |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Xiao, Leyuan |4 aut | |
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| 700 | 1 | |a Han, Hongju |4 aut | |
| 700 | 1 | |a Guo, Jiping |4 aut | |
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10.1007/s12665-024-11463-y doi (DE-627)SPR055087728 (SPR)s12665-024-11463-y-e DE-627 ger DE-627 rakwb eng 550 VZ 38.95 bkl Hu, Man verfasserin aut Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. Landslide (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 GNSS (dpeaa)DE-He213 Inclinometers (dpeaa)DE-He213 Numerical simulation (dpeaa)DE-He213 Smoothed Particle Hydrodynamics (SPH) (dpeaa)DE-He213 Ren, Yi aut Xiao, Leyuan aut Liu, Xiangyu aut Tan, Qiuting aut Han, Hongju aut Guo, Jiping aut Enthalten in Environmental earth sciences Springer Berlin Heidelberg, 2009 83(2024), 6 vom: März (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:83 year:2024 number:6 month:03 https://dx.doi.org/10.1007/s12665-024-11463-y lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 VZ AR 83 2024 6 03 |
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10.1007/s12665-024-11463-y doi (DE-627)SPR055087728 (SPR)s12665-024-11463-y-e DE-627 ger DE-627 rakwb eng 550 VZ 38.95 bkl Hu, Man verfasserin aut Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. Landslide (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 GNSS (dpeaa)DE-He213 Inclinometers (dpeaa)DE-He213 Numerical simulation (dpeaa)DE-He213 Smoothed Particle Hydrodynamics (SPH) (dpeaa)DE-He213 Ren, Yi aut Xiao, Leyuan aut Liu, Xiangyu aut Tan, Qiuting aut Han, Hongju aut Guo, Jiping aut Enthalten in Environmental earth sciences Springer Berlin Heidelberg, 2009 83(2024), 6 vom: März (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:83 year:2024 number:6 month:03 https://dx.doi.org/10.1007/s12665-024-11463-y lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 VZ AR 83 2024 6 03 |
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10.1007/s12665-024-11463-y doi (DE-627)SPR055087728 (SPR)s12665-024-11463-y-e DE-627 ger DE-627 rakwb eng 550 VZ 38.95 bkl Hu, Man verfasserin aut Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. Landslide (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 GNSS (dpeaa)DE-He213 Inclinometers (dpeaa)DE-He213 Numerical simulation (dpeaa)DE-He213 Smoothed Particle Hydrodynamics (SPH) (dpeaa)DE-He213 Ren, Yi aut Xiao, Leyuan aut Liu, Xiangyu aut Tan, Qiuting aut Han, Hongju aut Guo, Jiping aut Enthalten in Environmental earth sciences Springer Berlin Heidelberg, 2009 83(2024), 6 vom: März (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:83 year:2024 number:6 month:03 https://dx.doi.org/10.1007/s12665-024-11463-y lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 VZ AR 83 2024 6 03 |
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10.1007/s12665-024-11463-y doi (DE-627)SPR055087728 (SPR)s12665-024-11463-y-e DE-627 ger DE-627 rakwb eng 550 VZ 38.95 bkl Hu, Man verfasserin aut Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. Landslide (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 GNSS (dpeaa)DE-He213 Inclinometers (dpeaa)DE-He213 Numerical simulation (dpeaa)DE-He213 Smoothed Particle Hydrodynamics (SPH) (dpeaa)DE-He213 Ren, Yi aut Xiao, Leyuan aut Liu, Xiangyu aut Tan, Qiuting aut Han, Hongju aut Guo, Jiping aut Enthalten in Environmental earth sciences Springer Berlin Heidelberg, 2009 83(2024), 6 vom: März (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:83 year:2024 number:6 month:03 https://dx.doi.org/10.1007/s12665-024-11463-y lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 VZ AR 83 2024 6 03 |
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10.1007/s12665-024-11463-y doi (DE-627)SPR055087728 (SPR)s12665-024-11463-y-e DE-627 ger DE-627 rakwb eng 550 VZ 38.95 bkl Hu, Man verfasserin aut Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. Landslide (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 GNSS (dpeaa)DE-He213 Inclinometers (dpeaa)DE-He213 Numerical simulation (dpeaa)DE-He213 Smoothed Particle Hydrodynamics (SPH) (dpeaa)DE-He213 Ren, Yi aut Xiao, Leyuan aut Liu, Xiangyu aut Tan, Qiuting aut Han, Hongju aut Guo, Jiping aut Enthalten in Environmental earth sciences Springer Berlin Heidelberg, 2009 83(2024), 6 vom: März (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:83 year:2024 number:6 month:03 https://dx.doi.org/10.1007/s12665-024-11463-y lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.95 VZ AR 83 2024 6 03 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. 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Hu, Man |
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Hu, Man ddc 550 bkl 38.95 misc Landslide misc Monitoring misc GNSS misc Inclinometers misc Numerical simulation misc Smoothed Particle Hydrodynamics (SPH) Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China |
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550 VZ 38.95 bkl Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China Landslide (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 GNSS (dpeaa)DE-He213 Inclinometers (dpeaa)DE-He213 Numerical simulation (dpeaa)DE-He213 Smoothed Particle Hydrodynamics (SPH) (dpeaa)DE-He213 |
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ddc 550 bkl 38.95 misc Landslide misc Monitoring misc GNSS misc Inclinometers misc Numerical simulation misc Smoothed Particle Hydrodynamics (SPH) |
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Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China |
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Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China |
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Hu, Man Ren, Yi Xiao, Leyuan Liu, Xiangyu Tan, Qiuting Han, Hongju Guo, Jiping |
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landslide monitoring based on gnss and automated inclinometer systems and dynamic modeling using sph: a case study in qinghai, china |
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Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China |
| abstract |
Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Landslide monitoring based on GNSS and automated inclinometer systems and dynamic modeling using SPH: a case study in Qinghai, China |
| url |
https://dx.doi.org/10.1007/s12665-024-11463-y |
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Ren, Yi Xiao, Leyuan Liu, Xiangyu Tan, Qiuting Han, Hongju Guo, Jiping |
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Ren, Yi Xiao, Leyuan Liu, Xiangyu Tan, Qiuting Han, Hongju Guo, Jiping |
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10.1007/s12665-024-11463-y |
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2024-07-04T04:07:06.826Z |
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|
| score |
7.40158 |