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Spectral analysis of climate cycles to predict rainfall induced landslides in the western Mediterranean (Majorca, Spain)

Author

Listed:
  • Juan Antonio Luque-Espinar

    (Geological Survey of Spain (IGME), Regional Office in Granada)

  • Rosa María Mateos

    (Geological Survey of Spain (IGME), Regional Office in Granada)

  • Inmaculada García-Moreno

    (Geological Survey of Spain)

  • Eulogio Pardo-Igúzquiza

    (Geological Survey of Spain)

  • Gerardo Herrera

    (Geological Survey of Spain)

Abstract

In the present work, spectral analysis has been applied to determine the presence and statistical significance of climate cycles in long-term data series from different rainfall and gauging stations located in the Tramuntana Range, in the north-western sector of the island of Majorca. Climate signals recorded previously in the Mediterranean region have been identified: the ENSO, NAO, HALE, QBO and Sun Spot cycles as well as others related to solar activity; the most powerful signals correspond to the annual cycle, followed by the 6-month and NAO cycles. The incorporation of data derived from gauging stations contributes to better climate signal detection as local and exceptional influences are eliminated. Simulations have been performed for each rainfall/gauging station, using the most significant climate cycles obtained by means of the power spectrum. A good correlation between rainfall/flow values and simulated cycles has been obtained. The NAO and ENSO cycles are the most influential in the rainy periods, and specifically the NAO cycle, where a good correlation between episodes of high rainfall/flow and high values of ANAOI can be observed. At a second stage, landslides dated and recorded in the Tramuntana Range since 1954 (174 events) have been correlated with the simulated cycles obtaining good results, as the landslide events match rainfall peaks well. The correlation for the past decade (since 2005), when a detailed landslide inventory is available, also reveals a coincidence between landslide events and climate cycles, and specifically NAO and ENSO cycles. That is the case of the period 2008–2010, when numerous mass movements took place, and when the largest movement of the inventory was recorded. Results show a potential rainy period in the Tramuntana Range for the coming years (with maximum values around year 2021), when conditions similar to those related to the 2008–2010 event could take place again. The methodology presented in this work can contribute to the prediction of temporal, extreme hydrological events in order to design short-/medium-term mitigation strategies on a regional scale.

Suggested Citation

  • Juan Antonio Luque-Espinar & Rosa María Mateos & Inmaculada García-Moreno & Eulogio Pardo-Igúzquiza & Gerardo Herrera, 2017. "Spectral analysis of climate cycles to predict rainfall induced landslides in the western Mediterranean (Majorca, Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 985-1007, December.
    • Handle: RePEc:spr:nathaz:v:89:y:2017:i:3:d:10.1007_s11069-017-3003-3
    DOI: 10.1007/s11069-017-3003-3
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    References listed on IDEAS

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    Cited by:

    1. I. Fustos & R. Abarca-del-Rio & P. Moreno-Yaeger & M. Somos-Valenzuela, 2020. "Rainfall-Induced Landslides forecast using local precipitation and global climate indexes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 115-131, May.
    2. Mohammad Taghi Sattari & Fatemeh Shaker Sureh & Ercan Kahya, 2020. "Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1077-1094, July.
    3. Gianluca Mastrantonio & Giovanna Jona Lasinio & Alessio Pollice & Lorenzo Teodonio & Giulia Capotorti, 2022. "A Dirichlet process model for change‐point detection with multivariate bioclimatic data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.

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