Abstract
Since the “5.12” Wenchuan earthquake in 2008, frequent geologic hazards along the Longmenshan fault zone have had significant impacts on the socioeconomic conditions in the earthquake-stricken areas. Therefore, from the perspective of earthquake-induced hazards, this paper focuses on analyzing the change rules of disaster resilience under the spatial and temporal aggregation effects of earthquake-induced hazards, and this analysis provides an important basis for understanding the developmental characteristics of earthquake-induced hazards and disaster prevention, and mitigation after earthquakes. This paper takes Wenchuan County as an example. By collecting the 2008–2018 landslide geological hazards data, the global autocorrelation coefficient and local autocorrelation coefficient are adopted to analyze the temporal trends and spatial patterns of earthquake-induced hazards. At the same time, from the socioeconomic perspective, two disaster resilience indexes, the compatibility coefficient of industrial and employment structure and per capita GDP growth rate, were constructed to analyze the disaster resilience under the spatial and temporal aggregation effect of landslide geological hazards. The results show that, on the temporal trend, the temporal aggregation effect of earthquake-induced hazards has periodically decayed with time; in the spatial distribution, the spatial clustering effect as a whole increases first and then decreases, and the scope of the aggregation effect tends to narrow spatially. Disaster resilience (Hxy and RGDP) showed a trend of increasing first and then decreasing, and could not recover to the level before the earthquake in 2017, indicating that Wenchuan County was greatly affected by earthquake-induced hazards in the post-earthquake reconstruction process.
Similar content being viewed by others
References
Abbati SD, Cattari S, Lagomarsino S (2018a) Theoretically-based and practice-oriented formulations for the floor spectra evaluation. Earthq Struct 15(5):565–581
Abbati SD, D’Altri AM, Ottonelli D, Castellazzi G, Cattari S, Miranda S, Lagomarsino S (2018b) Seismic assessment of interacting structural units in complex historic masonry constructions by nonlinear static analyses. Comput Struct 213:51–71
Abramson DM, Stehling-Ariza T, Park YS, Walsh L, Culp D (2010) Measuring individual disaster recovery: a socioecological framework. Disaster Medicine and Public Health Preparedness. pp 46–54
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44
Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. CATENA 120:149–162
Berkes F, Ross H (2013) Community resilience: toward an integrated approach. Soc Nat Resour 26(1):5–20
Berkes F, Colding J, Folke C (2004) Navigating social–ecological systems: building resilience for complexity and change. Biol Conserv 119(4):581–581
Bivand RS (1998) A review of spatial statistical techniques for location studies
Bruneau M, Reinhorn A (2007) Exploring the concept of seismic resilience for acute care facilities. Earthquake Spectra 23(1):41–62
Burton C, Mitchell JT, Cutter SL (2011) Evaluating post-Katrina recovery in Mississippi using repeat photography. Disasters 35(3):488–509
Cardoni A, Cimellaro GP, Domaneschi M, Sordo S, Mazza A (2020) Modeling the interdependency between buildings and the electrical distribution system for seismic resilience assessment. Int J Disaster Risk Reduct:42
Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geosci 15(3):403–426
Carrara A, Guzzetti F (1995) Geographical information systems in assessing natural hazards
Carreño ML, Cardona OD, Barbat AH (2007) Urban seismic risk evaluation: a holistic approach. Nat Hazards 40(1):137–172
Chang SE, Shinozuka M (2004) Measuring improvements in the disaster resilience of communities. Earthquake Spectra 20(3):739–755
Chang Y, Wilkinson S, Brunsdon D, Seville E, Potangaroa R (2011) An integrated approach: managing resources for post-disaster reconstruction. Disasters 35(4):739–765
Cliff AD, Ord JK (1983) Spatial processes: models & applications. Econ Geogr 59(3):322
Corominas J, Matas G, Ruiz-Carulla R (2019) Quantitative analysis of risk from fragmental rockfalls. Landslides 16:5–21
Cutter SL (1996) Vulnerability to environmental hazards. Prog Hum Geogr 20(4):529–539
Cutter SL, Barnes LA, Berry M, Burton CG, Evans EG, Tate E, Webb JD (2008) A place-based model for understanding community resilience to natural disasters. Glob Environ Change Hum Pol Dimens 18(4):598–606
Dubin RA (1998) Spatial autocorrelation: a primer. J Hous Econ 7(4):304–327
Fan J, Chen JX, Tian B, Yan D, Cheng G, Cui P, Zhang W (2010) Rapid assessment of secondary disasters induced by the Wenchuan earthquake. Comput Sci Eng 12(1):10–19
Fan X, Domènech G, Scaringi G, Huang R, Xu Q, Hales TC, Francis O (2018) Spatio-temporal evolution of mass wasting after the 2008 Mw 7.9 Wenchuan earthquake revealed by a detailed multi-temporal inventory. Landslides 15(12):2325–2341
Fan X, Scaringi G, Domènech G, Yang F, Guo X, Dai L, He C, Xu Q, Huang R (2019) Two multi-temporal datasets that track the enhanced landsliding after the 2008 Wenchuan earthquake. Earth Syst Sci Data 11(1):35–55
Forster A, Jenkins G (2005) The assessment of landslide hazard potential as a guide to land use and planning in the South Wales Coalfield
Ganderton PT (2014) Disaster resilience. Social Science Electronic Publishing
García-Rodríguez MJ, Malpica JA, Benito B, Díaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95(3):172–191
Greco R, Sorriso-Valvo M, Catalano E (2007) Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study, Calabria, Italy. Eng Geol 89(1):47–66
Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1):181–216
Han PF, Tian SJ, Fan XY (2018) Statistical analysis and forecasting of the secondary disasters induced by Lushan earthquake. J Nat Disasters 27(1):120–126
Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118
Hosseini S, Barker K, Ramirez-Marquez JE (2016) A review of definitions and measures of system resilience. Reliab Eng Syst Saf 145(145):47–61
Huang R, Fan X (2013) The landslide story. Nat Geosci 6(5):325–326
Isaza-Restrepo PA, Carvajal HEM, Montoya CAH (2016) Methodology for quantitative landslide risk analysis in residential projects. Hab Int 53:403–412
Joerin J, Shaw R, Takeuchi Y, Krishnamurthy R (2014) The adoption of a climate disaster resilience index in Chennai, India. Disasters 38(3):540–561
Kammouh O, Gardoni P, Cimellaro GP (2020) Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks. Reliab Eng Syst Saf 198:106813
Kayastha P, Dhital MR, Smedt FD (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408
Klein RJT, Nicholls RJ, Thomalla F (2003) Resilience to natural hazards: how useful is this concept? Glob Environ Change Part B: Environ Hazards 5(1):35–45
Marino S, Cattari S, Lagomarsino S (2019) Are the nonlinear static procedures feasible for the seismic assessment of T irregular existing masonry buildings. Eng Struct 200:109700
Mavrouli O, Corominas J, Ibarbia I, Alonso N, Jugo I, Ruiz J, Luzuriaga S, Navarro JA (2019) Integrated risk assessment due to slope instabilities in the roadway network of Gipuzkoa, Basque. Country Hazards Earth Syst 19:399–419
Meerow S, Newell JP, Stults M (2016) Defining urban resilience: a review. Landsc Urban Plan 147:38–49
Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3):379–400
Michael-Leiba M, Baynes F, Scott G, Granger K (2012) Quantitative landslide eisk assessment of Cairns, Australia. Landslide Hazard Risk:621–642
Nagarajan R, Mukherjee A, Roy A, Khire MV (1998) Technical note temporal remote sensing data and GIS application in landslide hazard zonation of part of Western Ghat, India. Int J Remote Sens 19(4):573–585
Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204:287–301
Qigen L, Yanyi L, Lianyou L, Ying W (2017) Earthquake-triggered landslide susceptibility assessment based on support vector machine combined with Newmark displacement model. J Geo-Inf Sci
Qiu HJ (2014) Research on the spatial point pattern of geo-hazard–a case of Ningqiang county. J Arid Land Resour Environ 28(3):107–111
Ripley BD, Cliff AD, Ord JK (1984) Spatial processes: models and applications. J Am Stat Assoc 79(385):238
Sahebjamnia N, Torabi SA, Mansouri SA (2015) Integrated business continuity and disaster recovery planning: towards organizational resilience. Eur J Oper Res 242(1):261–273
Saori JN, Shu HL (2008) Thought and planning on reconstruction of urban green space after the Hanshin-Awaji earthquake in Japan (theory and example). Chin Landsc Archit
Shim JH, Kim CI (2015) Measuring resilience to natural hazards: towards sustainable hazard mitigation. Sustainability 7(10):14153–14185
Spiegler VLM, Naim MM, Wikner J (2012) A control engineering approach to the assessment of supply chain resilience. Int J Prod Res 50(21):6162–6187
Stahl T, Clark MK, Zekkos D, Athanasopoulos-Zekkos A, Willis M, Medwedeff W, Knoper L, Townsend K, Jin J (2017) Earthquake science in resilient societies. Tectonics 36(4):749–753
Tadić D, Aleksić A, Stefanović M, Arsovski S (2014) Evaluation and ranking of organizational resilience factors by using a two-step fuzzy AHP and fuzzy TOPSIS. Math Probl Eng 2014:1–13
Tang W, Li J, Lei Z, Wang E, Shen W (2015) Creating social–physical resilience to natural disasters: lessons from the Wenchuan earthquake. Nat Hazards 79(2):1111–1132
Tang C, Westen CJV, Tanyas H, Jetten VG (2016) Analysing post-earthquake landslide activity using multi-temporal landslide inventories near the epicentral area of the 2008 Wenchuan earthquake. Nat Hazards Earth Syst Sci 16(12):2641–2655
Timmerman P (1981) Vulnerability, resilience and the collapse of society: a review of models and possible climatic applications. Int J Climatol
Tongyue L (2017) New progress in study on resilient cities. Urban Plann Int 32(5):15–25
Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136
Ullsten O, Speth JG, Chapin FS (2004) Options for enhancing the resilience of northern countries to rapid social and environmental change. AMBIO 33(6):343–343
UNDRO (1979) Natural Disasters and Vulnerability Analysis. Office of the United Nations Disaster Relief Coordinator, Geneva, pp 5–9
Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice, Natural Hazards. UNESCO, Paris, p 63
Vona M (2020) A novel approach to improve the code provision based on a seismic risk index for existing buildings. J Build Eng:28
Wang J, Gu X, Huang T (2013a) Using Bayesian networks in analyzing powerful earthquake disaster chains. Nat Hazards 68(2):509–527
Wang LJ, Sawada K, Moriguchi S (2013b) Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Comput Geosci 57:81–92
Wang Y, Song C, Lin Q, Li J (2016) Occurrence probability assessment of earthquake-triggered landslides with Newmark displacement values and logistic regression: the Wenchuan earthquake, China. Geomorphology 258:108–119
Wen LI, Wenkai C, Zhonghong Z (2019) Analysis of temporal-spatial distribution of life losses caused by earthquake hazards in Chinese Mainland. J Catastrophol 34(1):222–228
Wu JL, Wang JF, Bin M, Xu-Hua L (2005) Spatial association analysis on epidemic of SARS in Beijing, 2003. J Zhejiang Univ Agric Life Sci 31(1)
Xu Q (2010) The 13 August 2010 catastrophic debris flows in Sichuan Province: characteristics, genetic mechanism and suggestions. J Eng Geol 18(5):596–608 (in Chinese)
Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274–287
Yang QS, Zhang HX, Bai W, Liu W (2018a) County-scale migration attractivity and factors analysis. In 2018 26th International Conference on Geoinformatics. pp 1–7
Yang Q, Chen W, Xu Y, Lv X, Zhang M, Jiang H (2018b) Polyphyllin I modulates MALAT1/STAT3 signaling to induce apoptosis in gefitinib-resistant non-small cell lung cancer. Toxicol Appl Pharmacol 356:1–7
Yao X, Li L (2016) Spatial-temporal assessment of debris flow risk in the Ms8.0 Wenchuan earthquake-disturbed area. J Disaster Res 11(4):720–731
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vectorm: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582
Zhou (2016) Research on Power Network Natural Disaster Early Warning Management Model and Decision Support System. North China Electric Power University, Beijing (in Chinese)
Zhou H, Wang J, Wan J, Jia H (2010) Resilience to natural hazards: a geographic perspective. Nat Hazards 53(1):21–41
Zhu Y, Li L, Zhao Y, Liang Z, Li H, Wang L, Wang Q (2018) Regional comprehensive drought disaster risk dynamic evaluation based on projection pursuit clustering. Water Policy 20(2):410–428
Zona A, Kammouh O, Cimellaro GP (2020) Resourcefulness quantification approach for resilient communities and countries. Int J Disaster Risk Reduct:46
Funding
The research in this paper is supported by the National Key R&D Program of China (2018YFC0604105), and the Sichuan Science and Technology Program (2019JDKY0017).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare that they have no conflict of interest.
Additional information
Responsible Editor: Philippe Garrigues
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, B., Han, S., Gong, H. et al. Disaster resilience assessment based on the spatial and temporal aggregation effects of earthquake-induced hazards. Environ Sci Pollut Res 27, 29055–29067 (2020). https://doi.org/10.1007/s11356-020-09281-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-09281-3