Abstract
Empirical thresholds indicating the meteorological conditions leading to shallow landslide triggering are one of the most important components of landslide early warning systems (LEWS). Thresholds have been determined for many parts of the globe and present significant margins of improvement, especially for the high number of false alarms they produce. The use of soil moisture information to define hydro-meteorological thresholds is a potential way of improvement. Such information is becoming increasingly available from remote sensing and sensor networks, but to date, there is a lack of studies that quantify the possible improvement of the performance of LEWS. In this study, we investigate this issue by modelling the response of slopes to precipitations, introducing also the possible influence of uncertainty in soil moisture provided by either field sensors or remote sensing, and investigating various soil depths at which the information may be available. Results show that soil moisture information introduced within hydro-meteorological thresholds can significantly reduce the false alarm ratio of LEWS, while keeping at least unvaried the number of missed alarms. The degree of improvement is particularly significant in the case of soils with small water storage capacity.
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Acknowledgements
The authors acknowledge the Civil Protection Agency of Campania and Servizio Informativo Agreometeorologico Siciliano (SIAS) for providing rainfall data. The research is part of the Ph.D. project “Modelling hydrological processes affecting rainfall-induced landslides for the development of early warning systems” within the Doctoral Course “A.D.I.” of Università degli Studi della Campania “L. Vanvitelli”. Most of the work was developed during Marino’s 6-month stay as a visiting researcher at the Section Water Resources of Delft University of Technology. The research has been also funded by Università degli Studi della Campania ‘L. Vanvitelli’ through the programme “VALERE: VAnviteLli pEr la RicErca”.
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Marino: bibliographic research, numerical experiments, writing (original draft)
Peres: rainfall generation, writing (original draft and review)
Cancelliere: rainfall generation, writing (review)
Greco: analysis of uncertainty, methodology, supervision, writing (original draft and review)
Bogaard: methodology, supervision, writing (original draft and review)
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Marino, P., Peres, D.J., Cancelliere, A. et al. Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach. Landslides 17, 2041–2054 (2020). https://doi.org/10.1007/s10346-020-01420-8
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DOI: https://doi.org/10.1007/s10346-020-01420-8